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Theme-II

Waste to Energy Technologies (WET)

Organized by

5th International Conference on Energy, Environment and Sustainable Development 2018 (EESD 2018)

A Multi-criteria Analysis of Options for Power Generation from Biomass in Pakistan

Lala Rukh Memona,*, Khanji Harijana, Nayyar Hussain Mirjata, Jonathan D. Nixonb

aMehran University of Engineering & Technology, Jamshoro, Pakistan bCoventry University, U.K.

Abstract

Pakistan is currently facing severe developmental challenges. Many of these challenges are interlinked to each other and ultimately to sustainable development. The key challenge, now, is to design such energy systems that reduce global emissions and bring energy, economic and ultimately social security at the minimum cost possible. Agriculture is one of the key economic sectors of the country, producing an abundant amount of bio-waste/biomass. The key barriers to bio-waste deployment to an optimal level in Pakistan is lack of in-depth multi-criteria case studies, due to which there is still no developed market for biomass in Pakistan, using Analytical Hierarchy Process of Multi-Criteria Decision Making (MCDM) science. AHP can be used to solve complex decision analysis that involves important yet conflicting aspects depending on various criteria. This method has applications in energy planning and decision making, as mega projects can no more be based on the merely low-cost assumption of resources availability. The study results based on a multi-criteria approach suggest that Bagasse is the best biomass resource to be utilized for clean electricity generation in the country, followed by Rice Husk, and Municipal Solid Waste. Whereas, Wood residue, animal dung, and poultry waste are highly undesirable for the purpose. © 2018 Lala Rukh Memon, Khanji Harijan, Nayyar Hussain, Jonathan Nixon. Selection and/or peer-review under responsibility of Energy and

Environmental Engineering Research Group (EEERG), Mehran University of Engineering and Technology, Jamshoro, Pakistan.

Keywords: AHP; Biomass; BOCR; Clean Power; MCDM; Pakistan; Sustainability

1. Introduction

Pakistan is currently facing several sustainable developmental problems; energy crisis, electricity shortfall, clean water

shortage, and challenges of climate change. These all issues are interlinked to each other as well to sustainable

development [1]. Now, the key challenge is to design such sustainable energy systems that reduce emissions and brings

energy and social security in the country at minimum cost possible. Bioenergy is being used to generate heat and

electricity in many countries of the world to make use of resources that are indigenously available, providing about 13%

of the total global primary energy supply [2]. This study attempts to identify the appropriate biomass resources for clean

electricity generation in Pakistan. Agriculture is one of the key economic sectors of the economy, contributing 21% to

Gross Domestic Product (GDP) and 43.5% to employment [3]. Pakistan’s livestock population of over 191 million can

also produce biomass (animal dung) to generate biogas which can be effectively used for cooking, heating and operating

the existing tube wells [4]. As such, the country has an enormous potential of bioenergy, to generate about 76% of the

country’s electricity demand, based on a 30% efficient power plants [5]. The key cause for high biomass resource

availability in the country is agricultural crop residue and high livestock population growth. Sugarcane is one of

Pakistan’s major crops of the country, producing an enormous amount of bagasse [6]. Furthermore, Alternate Energy

Development Board (AEDB) with the support of the World Bank is working on resource mapping of bioenergy in

Pakistan, focusing on sugar mills, rice mills, and municipal solid waste. Despite high resource availability, and serious

efforts for resource mapping being undertaken not much has been actually achieved, as biomass-based generation was

712 GWh in 2016-17 [7]. It is pertinent to mention here that the government of Pakistan had announced a policy back

in 2008, which particularly targeted private sector investment tending to reduce electricity demand-supply gap [8]. The

* Corresponding author. Tel.:92-332-2825558.

E-mail address: [email protected]

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policy also included a levelized tariff for the plants with minimum 28% thermal efficiency and with a minimum capacity

of 60 MW. Also, includes a detailed guideline for investors and a complete timeline to be followed for power

cogeneration. National Electric Power Regulatory Authority had given tariff under the referred framework. But, still

Bagasse; the only biomass in the country used for power generation accounts for only 0.58% of total generated electricity

in the country. Nevertheless, AEDB is taking genuine efforts and as per AEDB’s framework for “Power Cogeneration

2013”, over 300 MW of bagasse fired plants shall be developed across the country [9]. The government of Pakistan has

been considering only bagasse for electricity generation from biomass, based on its easy and low-cost availability. But,

biomass in the form of crop residue, livestock/poultry manure, Municipal Solid Waste (MSW), wood residue, and others

are also abundantly available to be utilized for electricity generation in Pakistan.

Biomass feedstock type selection is important rather complex decision. This study is an effort to identify the appropriate

biomass resources for clean power generation in Pakistan with the AHP-BOCR methodology. AHP is a sub-branch of

Multi-Criteria Decision Making (MCDM), which is an organized approach for decision-making under influence of

conflicting criteria. Because it is important rather complicated to incorporate different yet conflicting aspects.

Martínez used MCDM for the selection of the fuel to be gasified for a Fischer Tropsch reactor [10]. Sonal K.Thengane

[11] has also used AHP and Fuzzy AHP to compare 8 different hydrogen production technologies and the Fuzzy AHP.

Many energy system studies like; prioritizing renewable energy source for Turkey by using a hybrid MCDM and BOCR

methods [12], evaluation of solar energy projects [13], and selection of sustainable renewable energy source for North

Korea [14], have been carried out using AHP-BOCR methodology. Application of AHP-BOCR in the field of biomass

is used for effective evaluation of energy production from cellulosic biomass in Iran [15]. In energy systems decision

making, where a trade-off between BO and CR is required, AHP-BOCR framework supports the decision making. This

study, on an AHP-BOCR framework, evaluates appropriate biomass resource selection for clean electricity generation

in Pakistan.

Pakistan is potentially blessed with a diversity of biomass resources, and a number of studies are already carried out that

identify biomass potential from different biomass sources. A policy with incentives provided is available and even the

tariff is announced, but the utilization is yet minute. The key barrier to biomass deployment to an optimal level in

Pakistan is the lack of indigenous experience and expertise to identify multiple resources. In the case of Pakistan,

especially there is great potential for a biomass feedstock selection study to be carried out on a multi-criteria basis.

Since, low-cost availability is not the only factor to be considered while selecting a biomass feedstock source, similarly

not the environmental or social aspects alone. This study is an effort to analyze the diverse options for power generation

from biomass in Pakistan

Section 2 of this study briefs about the methodology of AHP-BOCR decision-making model, and analysis. Followed by

study results and discussion in section 3, finally, section 4 includes a conclusion based on this study.

2. Methodology

This AHP-BOCR decision-making model is developed after reviewing applications of MCDM for energy systems. The

criteria chosen are comparable to similar energy evaluation studies [16]. In order to provide credible weightings for the

selected pairwise comparisons of main and sub-criteria, seven policy, academic, and market experts; who specialize in

biomass energy have been engaged and their responses/priority rakings gathered through an online survey. The collected

data from survey and literature review is input to the MCDM model, which compares different biomass energy resources

for clean electricity generation in Pakistan based on Benefit, Opportunity, Cost, and Risk (BOCR) analysis. Multi-

criteria analysis has been used by decision-makers for energy planning for a very long time. Multicriteria approach is

ranking/ prioritizing of one or more alternatives from a pool of available alternatives based on multiple aspects and

criteria weights. MCDM does that thereby providing an effective framework for the evaluation of conflicting criteria

[17]. The MCDM analysis is carried out in Expert Choice Comparion® decision tool. The results of the model are the

priority ranking of each biomass resource alternative. Based on the MCDM results, recommendation for the appropriate

biomass resource selection is made.

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The AHP method prioritizes the alternatives by decomposing a complex goal into hierarchical levels and assigning

weightings based on respondents’ judgments of pairwise comparisons. The decision alternative with highest overall

weightings is certainly the most preferred one. Fig 1 illustrates the AHP-BOCR analysis hierarchy for this model. On

the top is the goal, then the hierarchy includes the four main criteria; BOCR, the next level includes BOCR cluster’s

sub-criteria as given in Table 1 and the final level includes all chosen alternatives; bagasse, rice husk, MSW, wood

residue, animal dung, and poultry waste in case of this study.

Fig. 1. Hierarchical representation of AHP-BOCR analysis

A critical literature review is required to choose sub-criteria and gather data related to all sub-criteria and alternatives’

electricity generation potential in order to take effective decisions.

Table 1. BOCR sub-criteria determined through a literature review

Sub-Criteria

B1 Energy Content of Feedstock

B2 Job Creation

B3 CO2 Emission Reduction

O1 Market Establishment

O2 Government Support/Funding

O3 Potential Sites for Resource Collection

C1 Required Feedstock for Unit Generation

C2 Feedstock cost

C3 Feedstock Storage & Maintenance Cost

C4 Feedstock Sourcing Cost

R1 Feedstock Availability

R2 Feedstock Growth Rate

R3 Local community support

It is important to understand here that final ranking of the alternatives completely depends on the ranking/weightings of

these main criteria and thirteen sub-criteria as in Table 1. Weightings of the BOCR and associated 13 sub-criteria is

obtained from survey response of policy, academic and market experts. Who prioritized the criteria based on the pair-

wise comparison on Saaty’s recommended 1-9 weighting scale [18]. The feedback from the survey respondents was

modeled using the Expert Choice Comparion® tool to compute the priority weightings. Since detailed mathematical

process and Expert Choice Comparion® working methodology is given elsewhere [19] [20], it is omitted from the paper.

The priority ranking of chosen alternatives was then evaluated based on input data to the AHP model. This was also

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carried out by pair-wise comparing all the alternatives with respect to each sub-criterion in every BOCR group. Each

pairwise comparison was based on the question: which alternative has the greater [21] or greater [2] factor? The

inconsistency level of the judgments are ensured to be well below 10% and is calculated by Saaty’s randomly generated

indexes [18].

This way, each biomass resource is given a priority score for each sub-criterion and a total rating for each BOCR group.

In an AHP-BOCR analysis, the overall outcome of alternative ranking can be evaluated in different ways [20]. But here,

the final outcome of alternative ranking is calculated by using “Eq. (1)”.

bB+oO-cC-rR (1)

Where, b, o, c, and r are the total weighing for each alternative against each main criterion, and B, O, C, and R are the

overall weightings for the Benefits, Opportunities, Costs and Risks groups, as determined by the survey respondents.

Since the equation subtracts the negative aspects (C and R) from positive aspects (B and O), the final outcome can be

negative, which doesn’t portray a wrong answer rather undesirability of the alternative under the given goal [22].

3. Results and Discussion

AHP-BOCR model development was accomplished using the Expert Choice Comparion® tool. Fig 2 shows the

weightings of the main-criteria; Benefit, Opportunity, Cost, and Risk, and Fig 3 provides the weightings of the pairwise

comparison of sub-criteria under each main-criterion. It is important to understand here that final rankings as shown in

Fig 4 of the alternatives (bagasse, rice husk, MSW, wood residue, animal dung, and poultry waste) completely depends

on the ranking/weightings of these four main criteria and thirteen sub-criteria based on the AHP-BOCR decision support

framework of this study.

3.1. Weightings of Main Criteria

Comparison of the four main criteria of this BOCR framework was assessed by survey respondents on the Saaty’s

fundamental scale [18, 19, 23]. The results as shown in Fig 2 illustrate the relative importance of weights of main criteria.

Fig. 2. Weightings of Four Main Criteria of the Study

These results are a reflection of priorities of a developing country like Pakistan. Doubtlessly, the benefit is highly

prioritized as compared to other, followed by opportunity closer to weightings of benefit. Cost is given a little lesser

importance compared to the first two. Whereas, it is also realized that risk is given not only the minimum weightings

comparatively but also the weighting of 13% is quite minimal as well. Nevertheless, it is a fact that in developing

countries where basic issues like health, education, and sanitary are yet not solved it is quite difficult and sometimes

impossible to consider attributes like a risk for even long-term planning.

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3.2. Weightings of Sub-Criteria

Comparison of the sub-criteria of this BOCR framework was assessed by survey respondents on the Saaty’s fundamental

scale [18, 19, 23] based on the respondent’ judgments, and the results of each of these are illustrated in Fig 3 (a-d)

Fig. 3. Weightings of sub-criteria of the study (Local Priority)

3.2.1. Benefit Sub-criteria (B1-B3)

Energy Content of feedstock (B1), Job creation (B2), and CO2 Emission Reduction (B3) was pairwise compared based

on benefit sub-criteria. The question asked in each comparison is that which of the two sub-criterion is more important

as a benefit for power generation from indigenous biomass resource. B2 is given almost 50% weightings, which

represent that job creation is highly prioritized as a benefit, followed by B1 and B3. The minimal weighting of CO2

emission reduction is again indicative of a developing country as shown in Fig 3 (a).

3.2.2. Opportunity Sub-criteria (O1-O3)

Market Establishment (O1), Government Support/Funding (O2), and Potential Sites for Resource Collection (O3) were

pairwise compared based on opportunity sub-criteria. The question asked in each comparison is that which of the two

sub-criterion is more important as an opportunity for power generation from indigenous biomass resource. The sub-

criterion Government Support/Funding (O2) is almost as equally preferred as Market Establishment (O1), followed by

Potential Sites for Resource Collection (O3) as shown in Fig 3 (b).

3.2.3. Cost Sub-criteria (C1-C4)

Required feedstock per unit generation (C1), Feedstock cost (C2), Feedstock Storage & Maintenance cost (C3), and

Feedstock sourcing cost (C4) were pairwise compared based on cost sub-criteria. The question asked in each comparison

is that which of the two sub-criterion is more important in terms of cost for power generation from indigenous biomass

resource. The sub-criterion Required feedstock per unit generation (C1) is highly prioritized followed by Feedstock

Storage & Maintenance cost (C3), Feedstock sourcing cost (C4), and Feedstock cost (C2) as shown in Fig 3 (c).

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3.2.4. Risk Sub-criteria (R1-R3)

Feedstock availability (R1), Feedstock growth rate (R2), and Local Community Support (R3) were pairwise compared

based on cost sub-criteria. The question asked in each comparison is that which of the two sub-criterion is more

important in terms of cost for power generation from indigenous biomass resource. The sub-criterion Local Community

Support (R3) is highly prioritized followed by Feedstock availability (R1), and Feedstock growth rate (R2) as shown in

Fig 3 (d).

3.3. Final Alternatives Ranking

It is evident from Fig 4 that Bagasse alternative with the highest weightings of 38.4% is found to be the best biomass

resource for clean electricity generation in Pakistan. It is followed by Rice Husk (21%), MSW (18.7%), Wood Residue

(13%), Poultry Waste (5.4%), and Animal Dung (4.2%).

Fig. 4. Determined weighting/ranking of six alternatives of the study

The final decision for Wood Residue, Poultry Waste, and Animal Dung turned out to be negative under bB+oO-cC-rR

analysis, which is a strong indication of the unsuitability of these alternatives under the goal of the study.

Based on the AHP-BOCR multi-criteria analysis, the final alternative ranking of biomass resources for clean power

generation in the country provides some interesting results. Bagasse has turned out to be the most favorite biomass

resource for the purpose, which is fortunately already being utilized in the country on a minute yet growing scale. But,

pertinent to note here is that rice husk is second most prioritized alternative and no any effort are evident to utilize such

a valuable resource, followed by MSW. There are some of the efforts evidence for MSW utilization but unfortunately,

not a single unit goes to the national grid from any of the biomass resources other than bagasse.

4. Conclusion

The future of biomass energy projects depends on Pakistan’s social, economic and technological development. This

study applied the MCDM method to conduct a cost-benefit analysis of six potential biomass resources of the country to

be utilized for clean electricity generation. Based on the results of this study, Bagasse is the most promising biomass

resource, Rice Husk and MSW are second and third most preferred biomass resources. Whereas, Wood Residue, Poultry

Waste, and Animal Dung with minimum weightings turned out to be highly undesirable with respect to the goal of the

study.

The AHP-BOCR framework implemented in this study has produced enlightening results on the ranking of main criteria,

sub-criteria, and based on these results final ranking of alternatives. These results can guide strategic decision makers

for considering Rice Husk and MSW as well as parallel to Bagasse for power generation in the country. Given adequate

consideration to these resources can help Pakistan meeting its’ electricity demand-supply gap, and solve associated

energy, economic and social security issues. However, this study suggests these results for the grid-connected electricity,

38.37979

20.90302

18.72406

13.16073

4.277343

5.365059

0 10 20 30 40 50

Bagasse

Rice Husk

MSW

Wood Residue

Animal Dung

Poultry Waste

Percentage

33


but these three highly prioritized alternatives can also be utilized for off-grid power generation to meet country’s power

demand, ultimately improving social security and moving a step forward to sustainability.

References

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and policies of Pakistan " Renewable and Sustainable Energy Reviews, vol. 79, pp. 110-127, 2017.

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Processes," 2016.

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technologies using AHP and Fuzzy AHP," International Journal of Hydrogen Energy, vol. 39, pp. 15293-15306, 2014.

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Conversion and Management, vol. 79, pp. 25-33, 2014.

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Sustainable Energy Reviews, vol. 15, pp. 554-563, 2011.

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cellulosic biomass in Iran," Economics, Management and Sustainability, vol. 2, pp. 104-119, 2017.

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749-761, 2006.

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34


Potential of Fruit Waste for Production of Renewable Source of

Energy

Saeed Ahmed , Shaheen Aziz , Abdul Qadeer ,Suhail A. Soomro

Chemical Engineering Department, Mehran University of Engineering & Technology Jamshoro,7605, Sindh Pakistan.

Abstract

Pakistan is an agricultural country having a variety of fruits which are grown in different seasons. A huge quantity

of fruit waste is generated from beverages and processed food industries having a high amount of carbohydrates

which have the potential to produce bioethanol through the fermentation process. Bioethanol demand is increasing

rapidly; to avoid looming shortage production rate of ethanol must be increased through cheaper and eco-friendly

methods. Fruit waste has high levels of sugars, including sucrose, glucose, and fructose that can be fermented for the

production of bioethanol. The chemicals which are used in this research are 5% of KMnO4, 1g urea, 50g sucrose,

and k.marxianus strain using different fruit wastes like apple, banana, orange, and grapes. Furthermore after

extraction of bioethanol the left residue from fermentation can be used for agriculture purpose. Effective production

of ethanol can be utilized by optimizing different fermentation parameters like temperature, PH, specific gravity and

concentration. The optimal bioethanol temperature is in the range of 25-40 degree centigrade, pH 4.3 to 5.4, specific

gravity 0.872 to 0.885 and concentration of bioethanol obtained from different samples are 4.73% to 6.21%. In this

article, the potential of different fruit waste for the renewable sources of energy through fermentation by use of yeast

Kluyveromyces marxianus has been reviewed and the result was compared through different parameters. The study

shows among different fruit wastes grapes, has higher efficiency than apple and banana.

© 2018 Saeed Ahmed, Shaheen Aziz, Abdul Qadeer, Suhail A. Soomro Selection and/or peer-review under responsibility of Energy and

Environmental Engineering Research Group (EEERG), Mehran University of Engineering and Technology, Jamshoro, Pakistan.

Keywords: bioethanol, renewable sources,kluyveromyce marxianus

1. Introduction

Ethanol is used globally as engine fuel and other fuel ingredients. Beside its medical usage, it is also used for

industrial as well as domestic purposes i.e., perfumes, cosmetics, hand wash etc. Ethanol can be produced from bio

waste like fruit waste, maize or wheat etc. are called bio ethanol [1]. bioethanol is artificial fuel and have more

hydroxyl group close with carbon particles. Bioethanol can be produced from waste that has presence of

carbohydrates in the addition of yeast called saccharomyces cerevisiae and kluyveromyces marxianus [2]. The en-

expensive and obtainable source to get bioethanol is fruit waste/ peels, because that have much amount of

carbohydrates. The efficiency to get ethanol is different in different fruits because of sugar content in it. Fruit peels

like banana, orange, mango, papaya, apple and grape’s efficiency can be measured through different parameters as

PH, temperature and specific gravity [3]. The fuel produced from organic waste or peels are called biofuel.

Bioethanol is alternate of gasoline and has no effect on environment [4]. Banana waste is most en-expensive source

for bioethanol production because of presence of sugar [5] Fruit waste is low cost raw material for the production of

bio fuel .by utilization of waste, treatment problem is also reduced. The leftover residue after extraction of biofuel

can be used for agricultural purpose [6].the main goal of this examination is to examine different yield of product

(bioethanol) got from different fruit waste.

* Corresponding author. Tel.: +92-346-3871332


35


2. MATERIALS AND METHODOLOGY:

2.1. Materials

All chemicals and glassware’s of laboratory grade were purchased from Alberuni Scientific Store Hyderabad and

Fruit Waste (FW) of orange, banana, and apple was collected from the Market Fresh Fruit Juice Shop.

2.2. Methodology

Experimental work was conducted at biochemical engineering laboratory, department of chemical engineering in

different phases. During the 1st step 200 grams of each FW was weighted separately and then washed with five

percent Pottasiumpermanate solution and further washed with distil water. After washing FW was crushed in

household grinder and then placed in separate beaker.

(a)

(b)

(c) Fig 1: peels: (a) Banana, (b) Apple, (c) Orange

During second step 1gram of urea, 50 grams of disaccharide and 10grams of brewer's yeast was added and mixed in

warm water. Prepared mixture was inoculum for fermentation process. Both mixtures were added and mixed in

conical flask. Same procedure was applied for all three wastes and were kept at 36 0C. During the fermentation

process in presence of zymase, sugar was converted in to alcohol and carbon dioxide gas. Hydrometer was used for

the frequently measuring the specific gravity of sample during the incubation. Reaching of the specific gravity value

at steady state position was indicating the end of the fermentation process. Incubation period were changed from one

FW to other FW.

Fig 2: recovery of product (a) distillation unit (b) Distillate

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Some fermented amount from each beaker were carried out for centrifuging, that results in the formation of

supernatant alcohol and rest of solution was examined through the distillation process as shown in Fig. 2. Ethanol

confirmation was through by iodine test. Some basic parameters of ethanol like concentration of bioethanol, pH,

Specific gravity and temperature were also examined.

3. RESULT AND DISCUSSION

In this research, it has been presented that the item was made from the organic product squanders. Along these lines,

the connection between present examination and pervious work was done by different parameters to analyze the

capability of ethanol creation using normal item misuse. The effect of different factors on the get sorted out of plant

material is given as underneath.

3.1. Effects of temperature

The Temperature has greater role in the making of bio-ethanol, as we increase the temperature, production of ethanol

also increases. The favorable ethanol’s temperature production goes between 25°C-40°C that relies upon normal

temperature. Bioethanol made from every orange and banana have temperature concerning 30 degree centigrade, and

aging liquor from apple have 28° centigrade ,papaya has 27°C temperature respectively, as shown in table 1. When the

temperature goes below the market, ethanol production is effected and their capacity to catalyze the alleged response

slows.

Table 1. Comparison at different temperature

Serial # Sample Temperature

(Previous exp. data )

Temperature

(Current Study )

1 Apple 4.4 4.3

2 Banana 5.2 5.1

3 Orange 4.8 4.7

Fig 3: Comparison at several temperature

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3.2. Effects of pH

The value of PH has great effect on alcohol production. pH of ethanol formed biologically from various fruit peels

waste were obtained. The pH estimation of bio-ethanol created by the maturation from 4-6. Yeast wants to live in a

light acidic condition that is with pH runs between 4-6 .The pH estimation of ethanol accomplished from natural

product squanders was given as, orange at PH is 4.9, apple PH is 4.5, and banana PH is 5.1. In the midst of these

ethanol produced using banana squanders had on cutting edge intoxicating substance (Table 2; Fig.4).

Table 2. Effect of production of bioethanol at different pH

Serial # Sample pH (Previous exp. Data) pH (Current study)

1 Apple 4.5 4.4

2 Banana 5.1 5.2

3 Orange 4.9 4.8

Fig. 4. Effect of production of ethanol at different PH

3.3. Effects of specific gravity

The Specific gravity is the factor used to determine the sugar level. As the fermentation initiated, the SP significantly

reduced and got a value of 0.860 at 48hrs and stayed continual. the particular gravity significantly reduced and

achieved a price of 0.860 at 48hrs and remained constant .The specific gravity of apple when aging was

decreased to 0.885 at 36 hrs and stayed steady. the specific gravity of papaya when aging was diminished to

0.872 at 72hrs, though the specific gravity of banana was decreased to 0.834 at 72hrs.. (Table 3; Fig.5).

Table 2. Effect of production of bioethanol at different Specific Gravity

Serial # Sample

Specific Gravity

(previos exp.data)

Specific Gravity

(Current study)

1 Apple 0.885 0.792

2 Banana 0.872 0.802

3 Orange 0.875 0.801

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Fig.4. The contrast of production of bioethanol at different Specific Gravity

3.4. Effects of Concentration

Ethonal content in distillation is measured as concentration of bio ethonal. Ethanol concentration is denoted by (%).

The bio-ethonal concentration was 5.3% in orange peels, Whereas, the bio-ethanol concentration in the apple sample

was found as 4.72% and ethanol present in banana was 5.40%. With the increase in sugar concentration, the ethanol

production increased significantly (Table 4; Fig.6).

Table 4. Effect of production of bioethanol at different Concentration

Serial # Sample

Concentration %

(Previos.exp data)

Concentration %

(Current study)

1 Apple 4.4 4.73

2 Banana 5.2 5.4

3 Orange 4.8 5.3

Fig. 6. Effect of production of bioethanol at different Concentration

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4. Conclusion:

The delayed consequences of current examination demonstrated that common items waste like orange,

banana and apple strips can be used as unrefined material for age of bio-ethanol.From this near

examination, the outcomes demonstrate that the greatest for creation of ethanol from banana at pH 5.4,

explicit gravity 0.860, temperature 30oC, and Concentration level of 6.21%.In last, the current study

concluded that bio-ethanol can be produced from different fruit wastes such as the maximum production

was resulted from banana waste as compared to apple and orange. This process is cost economic and does

not produce any poisonous leftovers and can be operated as small prototype business.

Acknowledgments

The writers are grateful to the Higher Education Commission and British Council to providing funds also Chemical

Engineering Department and Mehran UET Jamshoro for providing lab ;facilities to carry out this study.

References

[1]. Aziz, S., Shah, F.A., Rajoka, M.I. and Soomro, S.A., 2009. Production of ethanol by an indigenous wild and mutant strain of thermotolerant Kluyveromyces marxianus under optimized fermentation conditions. Pakistan Journal of Analytical & Environmental Chemistry, 10(1 & 2),

p.9.

[2]. Jatoi, A.S., Aziz, S., Unar, I.N., Soomro, S.A. and Siddique, M., 2018. Optimization of the Temperature Effect on Ethanol Production through use of Simulation. Journal of Applied and Emerging Sciences, 7(2), pp.pp139-144.

[3]. Schieber, A., Stintzing, F.C. and Carle, R., 2001. By-products of plant food processing as a source of functional compounds—recent

developments. Trends in Food Science & Technology, 12(11), pp.401-413. [4]. Akin-Osanaiye, B.C., Nzelibe, H.C. and Agbaji, A.S., 2005. Production of ethanol from Carica papaya (pawpaw) agro waste: effect of

saccharification and different treatments on ethanol yield. African Journal of Biotechnology, 4(7), pp.657-659.

[5]. Choi, I.S., Lee, Y.G., Khanal, S.K., Park, B.J. and Bae, H.J., 2015. A low-energy, cost-effective approach to fruit and citrus peel waste processing for bioethanol production. Applied Energy, 140, pp.65-74.

[6]. Alshammari, A.M., Adnan, F.M., Mustafa, H. and Hammad, N., 2011. Bioethanol fuel production from rotten banana as an environmental

waste management and sustainable energy. African Journal of Microbiology Research, 5(6), pp.586-598. [7]. Sharma, N., Kalra, K.L., Oberoi, H.S. and Bansal, S., 2007. Optimization of fermentation parameters for production of ethanol from kinnow

waste and banana peels by simultaneous saccharification and fermentation. Indian Journal of Microbiology, 47(4), pp.310-316.

[8]. Guerrero, A.B., Ballesteros, I. and Ballesteros, M., 2018. The potential of agricultural banana waste for bioethanol production. Fuel, 213, pp.176-185.

[9]. Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P. and

Qureshi-Emili, A., 2000. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), p.623. [10]. Longtine, M.S., Mckenzie III, A., Demarini, D.J., Shah, N.G., Wach, A., Brachat, A., Philippsen, P. and Pringle, J.R., 1998. Additional

modules for versatile and economical PCR‐based gene deletion and modification in Saccharomyces cerevisiae. Yeast, 14(10), pp.953-961.

[11]. Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L., Millar, A., Taylor, P., Bennett, K., Boutilier, K. and Yang, L., 2002.

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 415(6868), p.180. [12]. Velásquez-Arredondo, H.I. and Ruiz-Colorado, A.A., 2010. Ethanol production process from banana fruit and its lignocellulosic residues:

energy analysis. Energy, 35(7), pp.3081-3087.

[13]. Pesis, E., 2005. The role of the anaerobic metabolites, acetaldehyde and ethanol, in fruit ripening, enhancement of fruit quality and fruit deterioration. Postharvest Biology and Technology, 37(1), pp.1-19.

[14]. Jung, Y.H., Kim, I.J., Han, J.I., Choi, I.G. and Kim, K.H., 2011. Aqueous ammonia pretreatment of oil palm empty fruit bunches for ethanol

production. Bioresource technology, 102(20), pp.9806-9809. [15]. Arumugam, R. and Manikandan, M., 2011. Fermentation of pretreated hydrolyzates of banana and mango fruit wastes for ethanol production.

Asian J. Exp. Biol. Sci, 2(2), pp.246-256.

[16]. Dahnum, D., Tasum, S.O., Triwahyuni, E., Nurdin, M. and Abimanyu, H., 2015. Comparison of SHF and SSF processes using enzyme and dry yeast for optimization of bioethanol production from empty fruit bunch. Energy Procedia, 68, pp.107-116.

[17]. Guo, Y., Xu, J., Zhang, Y., Xu, H., Yuan, Z. and Li, D., 2010. Medium optimization for ethanol production with Clostridium

autoethanogenum with carbon monoxide as sole carbon source. Bioresource technology, 101(22), pp.8784-8789. [18]. Fernando, S.E.L., Bianca, Y.P.S., Sergio, S.T., Eapen, D. and Sebastian, P.J., 2014. Evaluation of agro-industrial wastes to produce bioethanol:

case study-mango (Mangifera indica L.). Energy Procedia, 57, pp.860-866.

[19]. Dhillon, G.S., Kaur, S. and Brar, S.K., 2013. Perspective of apple processing wastes as low-cost substrates for bioproduction of high value products: a review. Renewable and sustainable energy reviews, 27, pp.789-805.

[20]. Visioli, L.J., Stringhini, F.M., Salbego, P.R., Chielle, D.P., Ribeiro, G.V., Gasparotto, J.M., Aita, B.C., Klaic, R., Moscon, J.M. and Mazutti,

M.A., 2014. Use of agroindustrial residues for bioethanol production. In Bioenergy Research: Advances and Applications (pp. 49-56).

40


Investigation of Starch and Protein in “IRRI BROKEN RICE” by

Acid Hydrolysis and Kjeldahl Method

Basim Shaikh a, Shaheen Aziz Shaikh b, Asra Janb, Wali-Ur-Rahmanc

Department of Chemical, Mehran University of Engineering & Technology Pakistan 76080

Abstract

Acid hydrolysis is a unique and fast method for analysis of starch present in rice, this method is not so common

because it is used in industry due to limitation of time. Similarly, protein also present in rice so it can be investigated

by Kjeldahl method. Rice contains about 69-82% starch depend on nature and quality of rice. Irri broken rice was

selected for starch and protein analysis. Irri broken rice contain starch about 70-79% while protein contain about 7-

9% depend on quality of rice, mostly broken rice used for conversion of starch into glucose by enzymatic conversion.

The main objective is to obtain the starch and protein % present in Irri Rice on lab scale. Acid Hydrolysis depend on

Heating, filtration, neutralization and titration. Kjeldahl methods perform 3 steps for protein analysis Digestion,

Distillation and Titration.

© 2018 Basim Shaikh, Shaheen Aziz, Wali-ur-Rahman. Selection and/or peer-review under responsibility of Energy and Environmental

Engineering Research Group (EEERG), Mehran University of Engineering and Technology, Jamshoro, Pakistan.

Keywords: Acid Hydrolysis, Starch Analysis, Protein analysis, Kjeldahl method. Irri Broken rice.

1. Introduction

Production of rice is an important position in a country, rice cultivated over largest area of land for producing cereals,

about 90% rice produced in Asian states. One of the most necessary and useful cultivated grain on earth is production

of rice [1]. Many qualities and varieties of rice are farmed all over the world that would be used for many purposes.

Pakistan holds an extremely 2nd position in rice growing in agriculture and the national economic

expenditures. Pakistan is the 4th largest rice producer in the world, super Kernel, Irri and Basmati are major rice

cultivated source in Pakistan [3-2].

Fig 1. Shows the nature, quality, size and variety of rice grown in Pakistan. Rice are categorized as (brown and milled

rice). Rice is among second rank in the staple food grain crop in Pakistan and it is also the major source of foreign

exchange via exporting the rice and earning throughout the year. One-tenth of best quality of rice is export from

Pakistan. Most of the rice are cultivated in the region of Sindh and Punjab. Farmers are mostly reliable over rice

cultivation, as rice are their major source of employment. Major varieties of rice produced in the Pakistan include

Super Kernel Basmati, super Basmati, Kaynaat, and PK-198 Basmati. While some non-basmati varieties of rice

include; Long Grain PK-386, Irri-9 and Irri-6, Irri-6 B2, KSK133. Basmati rice is mostly cultivated in Punjab, while

non-basmati grain grown in region of Sindh province [2]. The main sources of starch and protein are corn, rice, wheat,

beat and potato but every specie is significantly different in composition, structure, physical and chemical properties

and retro gradation properties. Corn and rice contain higher amount of starch and protein rather than

wheat and potato [6].

* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 .


41


There are variety of broken rice that selected for extraction of starch such as KSK-133, Basmati-86, Basmati Super,

Kaynaat, IRRI-6, IRRI-6 B2, and KS-282. After survey in market got price of rice depend on quality of rice. Two

types of broken rice can be selected or used i.e. IRRI-6 & IRRI-6 B2 were due to economical point and contains high

percent of starch [11].

Rice usually contains 72-80% starch, so rice become one of potential sources of starch. Unfortunately, the information

about the process of production of rice starch is quite limited in research of low quality rice. This research is then

motivated by this reason specially to utilize the IRRI-6 & IRRI-6 B2 low grade Pakistani broken rice as the raw

material to produce the rice starch [7].

The use of rice starch as an additive in several food and industrial products as well as its mostly used in pharmaceutical

industry as raw sweetener or raw medicines. With the inherent merits of small and uniform size distribution of rice

starch and its white color and clean odor, deserts and bakery products are some of the favorable applications among

processed foods [4].

Maize is the largest starch producing source, with other commonly used sources like wheat, potato, tapioca and rice.

Two types of molecules are present in Starch i.e., amylose (normally 20–30%) and amylopectin (normally 70– 80%).

Amylose and amylopectin consist of polymers of α_Glucose units in the structure [5].

Fig. 1. Identification of Rice Grow in Pakistan

2. Material and Methods

2.1. Reagents and Chemical

Hydrochloric Acid (HCl 37% conc.) Methyl red indicator, Methyl blue indicator, Sodium Hydroxide (NaOH 10%

conc.) Fehling I & II, Copper Sulfate (Cu5SO4), potassium sulphate (K2SO4), Sulfuric Acid (H2SO4), Distilled water,

Sulfuric Acid (0.1N H2SO4), Sodium Hydroxide (NaOH 50% conc.) Bromocresol green indicator (C21H14Br4O5S),

Sodium Hydroxide (0.1N NaOH). The chemicals used for analysis are analytical grade.

2.2. Analysis of Starch

The analysis of starch in rice can be analyzed by Acid Hydrolysis method. IRRI broken rice were selected for

hydrolysis, samples were collected from various cities of Sindh Region.

2.3. Procedure for Starch Analysis

Procedure of Starch analysis is based on heating of sample, filtration, neutralization, titration and calculation of

Dextrose Equivalent Value.

42


2.3.1. Heating, cooling and filtration of sample

Initially, 03gm of rice sample has taken and make a fine powdered, add 200ml distilled water and add15ml HCL (37%

conc.) slowly. Shake flask by hand for about 5mints for homogenization.

HCL + NH3.H2O NH4CL + H2O

The round bottom flask was heated for about 02hrs and 30mints, during heating the above reaction occurred. NH4CL

is condensed and the volume of flask is maintained by condensation. During heating of sample all the solid dissolved

in a solution. Turn the flame off and let the sample be cooled at room temperature for about 30mints. Filter the sample

and obtained liquid sample about 200-220ml then added few drops of methyl red indicator. See Fig 2:

(b)

Fig. 2. (a) Heating of sample, and (b) filtered sample with indicator

2.3.2. Neutralization and titration of sample

Filtered sample is then neutralized with 10% NaOH, as the color changes to white stop neutralization, about 70ml

Sodium hydroxide is consumed for neutralization of sample. See Fig 3: Further distilled water is added in a sample in

order to makeup up to 500ml.

Fig. 3. Sample after neutralization

Fehling I & II solution (12.5ml) each is taken in a flask and heated over hot plate, as the chemical is boiled now add

few drops of methyl blue indicator and color changes to blue. As shown in Fig 4(a):

(a)

43


Neutralized sample was taken in a 100ml burette and fix in a titrate stand as shown in Fig 4(b):

Start titration slowly as the color changes from Blue to Dard Reddish color, titration stopped and note (ml) obtained.

Further titrated (ml) is used to calculated the DE of sample by Eq. 2

(a) (b)

Fig 4. (a) Fehling’s Solution in flask (b) titration with sample

2.3.3. Dextrose Equivalent

Table 1: shows the constant value range of (factor of ml) obtained from titration. Dextrose equivalent in starch can be

calculated by (ml) obtained while titration of sample. The starch % can be calculated by eq. 1, while DE can be

calculated first by eq.2

%𝑜𝑓 𝑆𝑡𝑎𝑟𝑐ℎ = 𝑚𝑔 𝐷𝐸𝑋 𝑥 5 𝑥 0.906 𝑥 100 𝑥 100

𝐺 𝑥 𝑇 𝑥 1000

𝑚𝑔 𝐷𝐸𝑋 = 𝑓𝑎𝑐𝑡𝑜𝑟 𝑜𝑓 𝑚𝑙 𝑥 100/𝑚𝑙

Table 1: Constant Value Range for factor of ml

16-18 ml 120.2

19- 21 ml 120.3

22-23 ml 120.4

24-26 ml 120.5

27-28 ml 120.6

29-30 ml 120.7

2.4. Analysis of Protein

Protein is a byproduct that is present in rice, protein can be analyzed by Kjeldahl method, an old and useful method

for protein analysis either present in any food material like maize, corn, rice, wheat, peas, meat etc.

2.5. Procedure for Protein Analysis

The Kjeldahl methods contain 3 steps for analysis i.e.

(01)

(02)

44


Digestion of sample

Distillation of sample

Titration of sample

2.5.1. Digestion of sample

Initially 02gm of rice sample has taken and make a fine powdered, then put sample in Kjeldahl (kj) flask and add 10-

12gm k2SO4, 30-40ml H2SO4 and 0.3-0.5gm CuSO4 (catalyst), the catalyst (CuSO4) is used to speed up the rate of

reaction. The nitrogen is present in rice, hence following reaction is occurred during digestion process. Fig 5: shows

the digestion process.

Organic N + H2SO4 (NH4)2SO4 + H2O + CO2

Fig. 5. Digestion/heating of sample

2.5.2. Distillation of Sample

Round bottom flask connected with NaOH vessel over the top, about 250-300ml of distilled water is added into a flask

and pour the digested sample in a flask. Heat the flask on low flame and add few solid granules of Zinc (Zn) as a

catalyst. The Zn reduce the rate of reaction and it also control the bubbling occur in flask during boiling. Mercury or

glass balls are also used instead of Zn. The flask is connected with condenser where fresh water is circulated inside

tubes. See Fig 6:

Fig. 6. Distillation of sample and neutralization of sample with NaOH

2.5.2.1. Neutralization of sample with NaOH while Distillation

NaOH Vessel

45


The round bottom flask contains a glass vessel on top, that is filled with 50% NaOH Conc. Gradually add the NaOH

in the distillation process until the color change into pink or reddish, as the color change stop the neutralization process

and continue heat the sample for more 02hrs. (NaOH should be added dropwise to be safe from any accident, due to

boiling of sample). Following reaction occur during neutralization process.

(NH4)2SO4 + 2NaOH 2NH3 + Na2SO4+ 2H2O

Formation of NH3 after neutralization with NaOH, as NH3 condensed, the temperature reduces and collected in a

beaker as shown in Fig 6:

2.5.3. Titration of sample with 0.1N of NaOH

About 200-250ml condensed NH3 sample obtained during distillation and neutralization. Increase the volume of

sample by adding distilled water, add few drops of Bromocresol green indicator, then sample is titrated with 0.1N

NaOH, titrate slowly as the color change from orange to dark reddish color stop the titration process and note the ml

obtained.

2.6. Nitrogen Determination during Experiment

Nitrogen% = ml 01.N of H2SO4 – ml 0.1N NaOH * (0.0014*100) (3)

Sample of wt.

% protein= Nitrogen% * 6.25 (4)


3.1. Starch analysis

Irri broken rice were selected for starch analysis, its rate is cheap 26-27/kg. Rice was purchased from different brokers

and from different land so the starch % is different. Rice samples obtained/collected from 3 different cities of Sindh

Province those are: Moro, Nawabshah, and Sukkur. So according to Table 2:

The rice sample that collected from Moro city was analyzed, starch % was obtained about 75.93± and total timed

consumed for overall analysis was about 03hrs. The rice sample that collected from Nawabshah city was analyzed,

starch % was obtained about 73.01%± and total time was consumed for analysis was about 03hrs. The rice sample

that collected from Sukkur city was analyzed, starch % was obtained about 70.75% and total time was consumed for

analysis was about 03hrs. 03 sample were analyzed and their results are different from each other due to quality of

rice, land impact on rice, water consumption by rice and land and quality of water.

3.2. Analysis of protein

Irri broken rice were selected for protein analysis, the rice contains about 6-12% protein depend on quality of rice,

the Irri rice contains about 7-10% protein. Table 2, shows the results of different rice sample.

The rice sample that collected from Moro city was analyzed, protein % was obtained about 7.43± and total timed

consumed for overall analysis was about 04hrs.

The rice sample that collected from Nawabshah city was analyzed, protein % was obtained about 7.87%± and total

time was consumed for analysis was about 04hrs

The rice sample that collected from Sukkur city was analyzed, protein % was obtained about 7.01% and total time

was consumed for analysis was about 04hrs.

46


Table2: Experimental Results for Starch and Protein

3.3. Comparison of Results

Bienvenido [5] performed various experiments on different type of rice like Brown rice, polished, milled etc. as the

results are compared with researcher milled rice was selected. Starch was analyzed by two methods i.e. commercial

and laboratory method. Time consumed for commercial method was about 24hrs, analysis contain several steps like

steeping, removal of cell wall, recovery of protein, washing and drying. Starch % obtained was about 95%, while

protein calculated was about 6-13%.

2nd method for analysis is laboratory method. This method requires 20-30days for starch analysis, initially sample is

treated with NaOH for 24Hrs, then filtration, and put it tube for long time until protein traces are negative, after

removal of protein sedimentation and centrifugation process performed.

Usman, Ishfaq, Malik [9] selected KSK-133 for analysis, the experiment was based on pH and temperature, and

stepping time was about 22hrs and obtained maximum starch was recovered as shown in Table 3:

Table 3. Starch Recovery in case of pH and Temperature

Temp. (°C) 22 25 30 35 40

pH Strach Recovery(%)

7 76.3 77.27 78.9 80.3 84.9

8 77.35 77.51 77.8 81.3 83.2

8.5 79.95 10 80.1 85.7 88.8

9 80 81.83 84.975 87.03 92

9.5 84.75 85.03 85.5 90.45 95

In table 3: researchers increase temperature gradually along with pH and obtained different results for starch recovery

at about 40℃ and 9.5 pH maximum starch recovered about 95%.

Conclusions

Acid Hydrolysis is technique by which Starch can be analyzed in limitation of time, every type of rice can be analyzed

via Hydrolysis. Following are the key findings.

Kjeldahl method can be used to analyze the protein present in Rice, even by this method other substance like

wheat, corn, maize, meat and peas can be analyzed. This is more accurate method of protein analyzation.

Irri broken Rice were selected for analyzation due to cheap and easily available. All the research was

performed in the Laboratory of Habib-ADM Ltd.

About 75.93% starch was present in rice sample that collected from Moro while protein was obtained 7.43%

73.01% starch was present in sample collected from Nawabshah and protein was about 7.87%

70.75% starch was present in sample collected from Sukkur and protein was about 7.01%

All the experiments performed with lab equipment’s manually like Titration, digestion, heating, filtration,

etc. so there may be ±2% Error.

The starch can further convert into various useful products by enzymatic conversion.

References

[1]. Daiana de Souza et al. (2015) “Characterization of rice starch and protein obtained by a fast-alkaline extraction method” Food Chemistry

191 (2016) 36–44

S.No City Name Starch % Protein %

01 Moro 75.93 ±2 7.43 ±2

02 Nawabshah 73.01 ±2 7.87 ±2

03 Sukkur 70.75 ±2 7.01 ±2

47


[2]. Luca Amagliani et al. (2016) “Composition and protein profile analysis of rice protein ingredients”

[3]. Essien Okon et al. (2015) “Investigating the Effect of Growth Regulator, Furolan on Starch and Protein Contents in Winter Wheat

cultivars” Vol. 41, No. 2–3, pp. 107–110

[4]. Sylvia Carolina ALCÁZAR-ALAY et al. (2015) “Physicochemical properties, modifications and applications of starches from different

botanical sources”

[5]. Bienvenido O Juliano (2015) Rice Starch: Production, Properties and Uses

[6]. S. BARBER et al. (1983) “CHEMICAL AND BIOLOGICAL DATA OF RICE PROTEINS FOR NUTRITION AND FEEDING”

[7]. Lan Wang et al. (2009) “Physicochemical properties and structure of starches from Chinese rice cultivars” Food Hydrocolloids’ 24 (2010)

208–216

[8]. Muhammad Usman et al. (2014) “Effects of Temperature, pH and Steeping Time on the Extraction of Starch from Pakistani Rice” Volume

5, Issue 6, June-2014

[9]. Muhammad Usman et al. (2014) “Alkaline Extraction of Starch from Broken Rice of Pakistan” Vol. 7 No. 1 July 2014, pp. 146-152

[10]. Industrial Starch Chemistry, Aksoy Mh. 1768 sk no:22/2 Karsiyaka Izmir, Turkey.

[11]. Emeje Martins Ochubiojo et al. (2012) Starch: From Food to Medicine

[12]. Starch; Chemistry and Technology, by James Be Miller, Roy whistler, 3rd Edition 2009

[13]. Abeera moin et al. (2017) “Influence of different molar concentrations of acid on morphological, physicochemical and pasting properties

of Pakistani Basmati and Irri rice starches” Biological Macromolecules 101 (2017) 214–221

[14]. Luca Amagliani et al. (2017) “The composition, extraction, functionality and applications of rice proteins” Trends in Food Science &

Technology 64 (2017) 1e12

[15]. Xiaoli Shu et al. (2014) “Effects of grain development on formation of resistant starch in rice” Food Chemistry 164 (2014) 89–97

[16]. Luca Amagliani et al. (2016) “Chemistry, structure, functionality and applications of rice starch” Journal of Cereal Science 70 (2016)

291e300

[17]. Khurshid et al. (1993) “CONTRIBUTION OF AREA AND YIELD OF TOTAL RICE PRODUCTION IN PAKISTAN - AN

ANALYSIS” Pak. J. Agri. sa; Vol. 30, No. I, 1993

[18]. Carbohydrates: Simple Sugars and complex chains

[19]. Wilma Aparecida et al. (2016) “Syrup production via enzymatic conversion of a byproduct (broken rice) from rice industry” v38i1.26700

[20]. Luca Amagliani et al. (2016) “Physical and flow properties of rice protein powders” Journal of Food Engineering 190 (2016) 1e9

[21]. ChangyuanWang et al. (2014) “Comparison of Two Methods for the Extraction of Fractionated Rice Bran Protein” Volume 2014, Article

ID 546345, 10 pages

[22]. A guide to Kjeldahl nitrogen determination methods and apparatus ‘

[23]. Rice Almanac, Source Book for One of the Most Important Economic Activities on Earth, fourth edition

[24]. Rice Sector, JCR-VIS Credit Rating Company Limited, January 2016

[25]. K.Lorenz et al. ( 1972) “Starch hydrolyzed under high pressure and temperature” contribution no. 736

48


Process Simulation and Optimization of Dimethyl Ether Production

from Synthesis Gas: A review

Abdul Jabbar Kalhoroa*, Shaheen Azizb, Suhail A.Soomroc, Imran Nazir Unard, Wali Ur Rehmane

*Corresponding Author email: [email protected] aDepartment of Chemical Engineering MUET, Jamshoro, Pakistan

eDepartment of Environmental Engineering, MUET, Jamshoro, Pakistan

Abstract

Dimethyl ether (DME) is a clean and versatile environmental friendly fuel. It can be produced from coal, biomass and

natural gas through synthesis gas (CO, H2). DME is very attractive option for transportation and cooking fuel and can

be use as a substitute of diesel and LPG. DME is a promising fuel with very negligible NOx, SOx and PM emissions. It

is also known as green fuel. DME production from syngas requires effective process simulation and optimization in

order to predict the process behavior under given set of conditions because syngas-to-DME process involves exothermic

reaction and catalyst temperature window is very narrow. In this review paper different DME synthesis processes are

studies while the particular focus on modern reactor technologies such as Reactive dividing wall column, double

membrane reactor and multi stage fluidized bed reactors. There are two methods of DME production process, first is

indirect method and the second is direct method. The relevant literature is reviewed for process simulation of DME

production from synthesis gas and optimized processes conditions are studies in this review paper.

© 2018 “Abdul Jabbar Kalhoro, Shaheen Aziz, Suhail Ahmed Soomro, Imran Nazir Unar, Wali ur Rehman

Keywords: Dimethyl ether (DME);Synthesis gas; Process simulation; DME production

1. Introduction

Dimethyl ether (DME) is a clean and versatile fuel. It can be used to overcome energy supply demands and reducing

global environmental problems. There are many sources of DME production such as coal, Natural gas, raw petroleum,

and biomass [1]. DME can be used in different applications such as transportation fuel, chemical feedstock, cooking

fuel and power generation. DME is very attractive option for transportation and cooking fuel and can be use as a

substitute of diesel and LPG [2,3]. The cetane number of DME (55-60) is higher as compare to diesel. Its properties are

very similar to that of LPG which makes DME as appealing option for domestic cooking fuel [3]. Dimethyl ether is

environmental friendly fuel and also known as a blue fuel. The emissions such as SOx and NOx from DME fuel are very

negligible as compare to other petroleum-based fuel [4].

Table 1. Comparison of DME with other fuels [4]

DME Diesel Methanol

Formula CH3OCH3 - CH3OH

Normal boiling point (0C) -24.9 125-360 64

Density (g cm3)

Exergy (MJL-1)

Carbon content (wt %)

Cetane number

Ignition temperature (K)

Molecular weight (g mol-1)

0.661

20.63

52.2

55-60

623

46.07

0.856

33.32

87

40-55

483

198.4

0.792

17.8

37.5

5

743

32.4

*Abdul Jabbar Kalhoro

E-mail: [email protected]

49

mailto:[email protected]


The major source of DME (Dimethyl ether) fuel is coal through gasification. Coal is first converted in to syngas (CO,

H2) through gasification process then syngas is converted in to DME. Natural gas is another important source for DME

production. Syngas gas from natural gas is obtained through reforming and then it converted in to DME. Biomass can

also be used to produce DME via gasification process.

Fig. 1. (a) DME applications (b) Sources of DM

1.1. Advantages of DME as an alternative fuel

DME does not generate volatile peroxides and can be easily transported and stored.

CO, CO2 and unburned hydrocarbon emissions are very low from DME fuel as compare to natural gas, diesel and

other petroleum based fuels.

It is not a green house gas.

Due to comparable vapour pressure to LPG, it can easily transport and stored in current existing infrastructures.

It is non-carcinogenic and non-toxic

2. DME production process

There are two methods of DME production process, first is indirect method and the second is direct method. Both these

methods involve synthesis gas as intermediate step. In Indirect method methanol is produced from synthesis gas through

methanol synthesis and then DME is produced through dehydration of methanol. The second method called as direct

method does not require methanol syntheses, in this method Dimethyl ether is directly produced from synthesis gas.

Both these process require synthesis gas as a basic raw material, which can be produced from variety of feedstock’s and

its composition is varied according to its source. Direct method is more efficient and economical suitable as compare to

indirect method, but it is also very difficult to handle the process [5].

50


Fig. 2. Indirect and direct DME production process

2.2 Reactions involve in the DME production process

Reactions involve in DME production process are:

Methanol synthesis

2CO + 4H2 2CH3OH (1)

Methanol dehydrogenation

2CH3OH CH3OCH3 + H2O (2)

DME synthesis

3CO +3H2 CH3OCH3 +CO2 (3)

Water gas shift reaction

CO + H2O H2 + CO2 (4)

3. Process Simulation

DME production from syngas requires effective process design and simulation in order to get maximum yield. Process

simulation is very useful for design, simulate and optimization of simple to complex chemical processes. Process

simulation model can be used for finding optimum process conditions for maximum process efficiency. Process

simulation study can also be useful for sensitivity analysis and economic analysis of process. Aspen plus, PRO/II and

Chemcad software’s are very useful tools for simulation study of synthesis gas to DME synthesis process.


Synthesis

Gas

Methanol

Synthesis

DME

Synthesis

DME Methanol

Indirect method

Synthesis

Gas

DME

Synthesis DME

Direct method

51


Abashar studied DME production in single stage and multi-stage fluidized bed catalytic reactors and simulation results

shows DME yield is increased in multi-stage fluidized bed reactors as compare to single bed reactors up to 32.57 %.

Reactor performance is increased by using bifunctional catalyst (CuO-ZnO-Al2O3 / HZSM-5). Carbon monoxide (CO)

content in feed composition is important parameter because it provides the carbon source in reaction [6].

Farsi et al., simulated double membrane reactor for DME synthesis process. Obtained results are compared with

available data from traditional reactors. Obtained results illustrate, DME yield is enhanced around 17.2% in simulated

reactor. Higher DME yield is the primary focal point in his research [7].

Table 2. Performance of traditional and proposed reactor [7]

Traditional Proposed Reactor

MeOH (Conversion) 81.91 % 87%

DME mole flow(K mol / hr) 2454 2586

DME outlet (mole fraction) 0.443 0.55

Sumalatha et al., simulated Dimethyl ether (DME) process plant using aspen plus software. Methanol is used as primary

raw material for the process. The plant production is 80,000 tonnes of Dimethyl ether per year. Acid zeolite catalyst is

used in DME synthesis reactor. Simulation results show 99.9 % DME purity from methanol synthesis. NRTL property

package is selected in simulation and distillate rate and reflux ratio are selected as variable parameters while assuming

methanol conversion is 80% in DME synthesis reactor [8].

Fig. 3. DME production from methanol

Farsi developed dynamic model of Isothermal fixed bed reactor which is used in Dimethyl ether synthesis process. The

main parameter for feed stream is feed composition which is validated by sensitivity analysis of the process. PID

controllers are used to evaluate control of process and maintain suitable production rate. Fine tuned PID controller is

used for manipulate variable for cooling water pressure and efficiency is evaluated by disturbance rejection and set point

tracking. The results shows proper dynamic control of system is important for maintain process at suitable conditions

[9].

Anton and David developed reactive dividing wall column process and the outcome of this research work demonstrates

higher Dimethyl ether purity and reducing overall process costs. The process is simulated and optimized in Aspen Tech

Aspen Plus software. Proposed process simulation results show 12-58% energy savings, 60% reduced CO2 emissions

and reducing 30 % overall capital cost [10].

52


Fig. 4. (a) convensional process (b) Reactive dividing wall column

Kabir and Bhattacharya successfully simulated Dimethyl ether production from coal based synthesis gas. The kinetic

model LHHW (Langmuir Hinshelwood Hougen Watson) is selected for DME production reactor. Process simulation

results indicate that in order to achieve high yield of DME it is better to remove CO2 from fuel gas feed stream before

entering to reactor. Results also show that DME yield is maximum, when CO and H2 molar ratio is 0.81 and 1.41 in

syngas feed stream before entering reactor. It is further established that proper catalyst selection and proper heat

integration for process can result in higher DME yield. The property package SRK (Soave redlich Kwong) used for

process simulation of DME production from synthesis gas [11].

Farsi and Jahanmiri recommended membrane reactor with fixed bed and water cooling system for substantial DME

synthesis. The design of reactor is designed on a one-dimensional basis based on energy conservation and mass

preservation law in a steady state. The simulated results are compared with commercial adiabatic reactor and its results

shows that efficiency of the membrane reactor is improved 6.2 %. The proposed membrane reactor results show product

quality is higher as compared to commercially available reactors also catalyst life is substantially increased [12].

Chiu et al., developed process simulation of coal to methanol process using Pro/II software. Process consists of two

main units one is coal gasification and other is methanol synthesis unit. DME synthesis unit is also included in case

study model. Simulation results show Gross efficiency of process is 77.7%, while net efficiency is 63.3%. Dimethyl

ether production rate is 7.2 t/day and methanol production is 2784 t/day. The overall simulation result indicates that this

proposed model for methanol production have high efficiency as compared to other IGCC plants [13].

Lu, Teng and Xiao used lab scale fluidized bed reactor to analyse synthesis gas conversion in to DME. Experiments

results show that fluidized bed reactor efficiency is higher than other reactors such as fixed bed and slurry phase. All

important process parameters are calculated based on P-M model [14].

Shim et al., used Aspen plus software to build process simulation model for direct DME synthesis. DME production

process data is obtained and validated from plant situated in Korea. Process is assumed at steady state and property

package used for simulation is SRK (Soave Redlich Kwong). Obtained simulation results shows ideal temperature range

for DME synthesis is between 265-275 °C. Furthermore, shim concluded that the H2/CO ratio for maximum DME yield

should be between 1-1.5 [15].

Table 3. Experimental and simulation results [15]

Inlet

Case 1

Exp

Sim

Inlet

Case 2

Exp

Sim

CO2 0.08 0.146 0.134 0.080 0.151 0.135

53


H2 0.141 0.028 0.026 0.143 0.028 0.026

(Mol %) CO 0.377 0.313 0.316 0.374 0.314 0.311

DME

Methanol

-

-

0.044

0.002

0.042

0.007

-

-

0.044

0.001

0.043

0.007

XH2 (%) 79.6 81.7 80.4 81.8

XCO (%) 17.2 16.4 16.0 16.8

YDME (%) 19.2 18.3 19.4 18.9

PDME (kg/hr) 5.7 5.5 5.3 5.6

5. Conclusion

DME produdction process from syngas can be simulated using different process simulation softwares such as aspen

plus, PRO/II, Chemcad. Soave-Redlich-Kwong (SRK) property package is suitable for porcess simulation for DME

synthesis from syngas. Important parameters for DME production process from syngas are temperature, pressure and

H2/CO ratio and suitable operating conditions are 60-65 kg/cm2 pressure, 1-1.5 H2/CO ratio and 265-270 0C

temperature. In order to achieve high yield of DME it is better to remove CO2 from fuel gas feed stream before entering

to reactor and proper catalyst selection and appropriate heat integration for process can result in higher DME yield.

DME production efficiency is higher in Fluidized bed reactor as compare of fixed bed and slurry phase reactors.

Researchers suggested different methods for increasing DME yield such as Reactive dividing wall column, double

membrane reactor and multi-stage fluidized bed reactors. Direct DME synthesis process is economically suitable as

compare to indirect methanol synthesis process; though detailed research is required for particular reactor design and

selection for direct DME synthesis.

References

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

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H.M. Chiu, P.C. Chen, Y.P. Chyou, C.S. Yu, ”Process simulation study of coal to methanol based on gasification technology”, World

Academy of Science, Engineering and Technology, vol. 41, pp. 988-996, 2010. W. Lu, L. Teng and W. Xiao, “Simulation and experiment study of Dimethyl Ether syntheses from syngas in a fluidized bed reactor”,

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H. M. Shim, S. J. Lee, Y. D. Yoo, Y. S. Yun and H.T. kim, “Simulation of DME synthesis from coal syngas by kinetics model”, Korean Journal of Chemical Engineering, vol. 26, pp. 641-648, 2009.

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