Research Article
Thermodynamic Optimization of a Biogas Engine Powered Cogeneration System with Using Genetic Algorithm
Abstract
In this study a genetic algorithm based thermodynamic
optimization of biogas engine powered cogeneration system that is active in
GASKİ WWTP and produce electricity from biogas which is produced in plant will
be presented.
In this purpose, we will develop our own code that is suitable to
characteristics of system, by using MATLAB software. Biogas engine powered cogeneration system produces 1000 kW electricity and
supplies heat for anaerobic digestion. The focus of optimization will be on
exergetic efficiencies of heat exchangers and overall system. Optimization
variables are selected as output temperature of water that flow from digestion
throught engine-side heat exchanger, its mass flow rate and temperature of
water that transfer heat from engine
to heat exchanger. Optimization is
applied with using elitism and roulette
wheel methods seperately, also single point mutation is applied. By using
elitism and roulette wheel, exergetic efficiency of heat exchanger-1 found
respectively 32,2 % and 39,1 %. Exergetic efficiency of heat exchanger-2 for
both two methods found 59,5%. By using elitism and roulette wheel, exergetic
efficiency of exhaust gas heat exchanger
found respectively 47,7 % and 41%. Exergetic efficiency of overall
cogeneration system obtained 26,4 % by using elitism and 26,1 % by using
roulette wheel. These results discussed according to effects of variables by
using graphs.
References
- Referans1 Demirci, G., Türkavcı, L. 2001. Biyogaz ‘ Atıklardan Enerji’. Ankara: Temiz Enerji Vakfı.
- Referans2 Abubakar, M. M. 1990. Biogas generation from animal wastes. Nigerian Journal of Renewable Energy 1, 69-73
- Referans3 Lee, H. C., Mohamad, A. A., Jiang L. Y. 2017. A detailed chemical kinetics for the combustion of H2/CO/CH4/CO2 fuel mixtures. Fuel 193, 294-307.
- Referans4 Qdais, H. A., Hani, K.B., Shatnawi, N. 2010. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling 54, 359-363.
- Referans5 Martinez, E., Marcos, A., Al-kassir, A., Jaramillo, M. A., Mohamad, A. A. 2012. Mathematical model of a laboratory-scale plant for slaughterhouse effluents biodigestion for biogas production. Applied Energy 95, 210-219.
- Referans6 Kim, M., Yoo, C. 2014. Multi-objective controller for enchancing nutrient removal and biogas production in wastewater treatment plants. Journal of the Taiwan Institute of Chemical Engineers 45, 2537-2544.
- Referans7 Atia, D. M., Fahmy, F. H., Ahmed, N. M., Dorrah, H. T. 2012. Optimal sizing of a solar water heating system based on a genetic algorithm for an aquaculture system. Mathematical and Computer Modeling 55, 1436-1449.
- Referans8 Verdaguer, M., Molinos-Senante, M., Poch, M. 2016. Optimization management of substrates in anaerobic co-digestion: An ant colony algoritm approach. Waste Management 50, 49-54.
- Referans9 Li, H., Nalim, R., Haldi, P.A. Thermal-economic optimization of a distributed multi-generation energy system-A case study of Beijing, Applied Thermal Engineering, 26 (2006) 709-719.
- Referans10 Kılkış, B., Kılkış, Ş., New exergy metrics for energy, environment and economy nexus and optimum design model for nearly-zero exergy airport (nZEXAP) systems, Energy, 140 (2017) 1329-1349.
- Referans11 Dumont, O., Dickes, R., Rosa, M. D., Douglas, R., Lemort, V., Technical and ecomomic optimization of subcritical, wet expansion and trancritical Organic Rankine Cycle (ORC) systems coupled with a biogas power plant. Energy Conversion and Management, 157 (2018) 294-306.
- Referans12 Yağlı, H., Koç, Y., Koç, A., Görgülü, A., Parametric optimization and exergetic analysis comparison of subcritical and supercritical Organic Rankine Cycle (ORC) for biogas fuelled combined heat and power (CHP) engine exhaust gas waste heat, Energy, 111 (2016) 923-932.
- Referans13 Khaljani, M., Saray, R. K., Bahlouli, K. 2015. Thermodynamic and thermoeconomic optimization of an integrated gas turbine and organic Rankine cycle. Energy 93, 2136-2145.
- Referans14 Svanandam, S. N., Deepa, S. N., 2008, Introduction to Genetic Algorithms, Berlin: Springer.
- Referans15 Sakawa, M., 2002, Genetic algorithms and fuzzy multi-objective optimization, New York: Springer Science + Business Media.
- Referans16 Haupt, R. L., Haupt, S. E, 2004. Practical Genetic Algorithms, 2nd edition, New Jersey: Wiley-Interscience Publication.
Biyogaz Motorlu bir Birleşik Isı ve Güç Üretim Sisteminin Genetik Algoritma Yöntemi ile Optimizasyonu
Abstract
Bu çalışmada Gaziantep Atık
Su Arıtma Tesisine bağlı olarak çalışan ve tesiste atık su çamurundan üretilen
biyogazı kullanarak elektrik üreten biyogaz motorlu bir kojenerasyon sisteminin
genetik algoritma temelli termodinamik optimizasyonu yapılmaktadır. Bu amaçla
MATLAB programı kullanılarak kojenerasyon sistemi karakteristiğine uygun sistem
kodları geliştirilmiştir. Kojenerasyon sistemi 1000 kW elektrik üretmekte ve
havasız çürütme tankına gerekli ısıyı sağlamaktadır. Sistem ısı
değiştiricilerinin ekserji verimi optimizasyonu ile birlikte bütün sistemin
ekserji verim optimizasyonu hedef alınmıştır. Optimizasyon değişkeni olarak,
çürütme tankına ısı aktarımı sağlayan suyun ısı değiştiricisinden çıkış
sıcaklığı ve kütle akış debisi, ayrıca biyogaz motoru soğutma suyunun ısı
değiştiricisine giriş sıcaklığı seçilmiştir. Elitlik ve rulet çarkı yöntemleri
ile ayrı ayrı optimizasyon yapılmış olup, tek nokta mutasyon yöntemi
uygulanmıştır. Elitlik yöntemi ile optimizasyonda 1 numaralı ısı değiştiricinin
ekserji verimi %32,5, rulet çarkı yönteminde ise %40 olarak bulunmuştur. Her
iki yöntem için 2 numaralı ısı değiştiricinin ekserji verimi %59,5 olarak
hesaplanmıştır. Egzoz gazı ısı değiştiricisinin ekserji verimi ise elitlik ve
rulet çarkı yöntemlerine göre sırasıyla %44,7 ve %41,1 olarak bulunmuştur.
Kojenerasyon sisteminin toplam ekserji verimi ise elitlik yöntemi ile %26,5 ve
rulet çarkı yöntemiyle %26,1 olarak bulunmuştur. Yapılan analiz neticesinde
değişkenlerin etkileri grafikler halinde sunulmuş ve tartışılmıştır.
References
- Referans1 Demirci, G., Türkavcı, L. 2001. Biyogaz ‘ Atıklardan Enerji’. Ankara: Temiz Enerji Vakfı.
- Referans2 Abubakar, M. M. 1990. Biogas generation from animal wastes. Nigerian Journal of Renewable Energy 1, 69-73
- Referans3 Lee, H. C., Mohamad, A. A., Jiang L. Y. 2017. A detailed chemical kinetics for the combustion of H2/CO/CH4/CO2 fuel mixtures. Fuel 193, 294-307.
- Referans4 Qdais, H. A., Hani, K.B., Shatnawi, N. 2010. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling 54, 359-363.
- Referans5 Martinez, E., Marcos, A., Al-kassir, A., Jaramillo, M. A., Mohamad, A. A. 2012. Mathematical model of a laboratory-scale plant for slaughterhouse effluents biodigestion for biogas production. Applied Energy 95, 210-219.
- Referans6 Kim, M., Yoo, C. 2014. Multi-objective controller for enchancing nutrient removal and biogas production in wastewater treatment plants. Journal of the Taiwan Institute of Chemical Engineers 45, 2537-2544.
- Referans7 Atia, D. M., Fahmy, F. H., Ahmed, N. M., Dorrah, H. T. 2012. Optimal sizing of a solar water heating system based on a genetic algorithm for an aquaculture system. Mathematical and Computer Modeling 55, 1436-1449.
- Referans8 Verdaguer, M., Molinos-Senante, M., Poch, M. 2016. Optimization management of substrates in anaerobic co-digestion: An ant colony algoritm approach. Waste Management 50, 49-54.
- Referans9 Li, H., Nalim, R., Haldi, P.A. Thermal-economic optimization of a distributed multi-generation energy system-A case study of Beijing, Applied Thermal Engineering, 26 (2006) 709-719.
- Referans10 Kılkış, B., Kılkış, Ş., New exergy metrics for energy, environment and economy nexus and optimum design model for nearly-zero exergy airport (nZEXAP) systems, Energy, 140 (2017) 1329-1349.
- Referans11 Dumont, O., Dickes, R., Rosa, M. D., Douglas, R., Lemort, V., Technical and ecomomic optimization of subcritical, wet expansion and trancritical Organic Rankine Cycle (ORC) systems coupled with a biogas power plant. Energy Conversion and Management, 157 (2018) 294-306.
- Referans12 Yağlı, H., Koç, Y., Koç, A., Görgülü, A., Parametric optimization and exergetic analysis comparison of subcritical and supercritical Organic Rankine Cycle (ORC) for biogas fuelled combined heat and power (CHP) engine exhaust gas waste heat, Energy, 111 (2016) 923-932.
- Referans13 Khaljani, M., Saray, R. K., Bahlouli, K. 2015. Thermodynamic and thermoeconomic optimization of an integrated gas turbine and organic Rankine cycle. Energy 93, 2136-2145.
- Referans14 Svanandam, S. N., Deepa, S. N., 2008, Introduction to Genetic Algorithms, Berlin: Springer.
- Referans15 Sakawa, M., 2002, Genetic algorithms and fuzzy multi-objective optimization, New York: Springer Science + Business Media.
- Referans16 Haupt, R. L., Haupt, S. E, 2004. Practical Genetic Algorithms, 2nd edition, New Jersey: Wiley-Interscience Publication.
There are 16 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | April 30, 2019 |
| Submission Date | July 4, 2018 |
| Acceptance Date | February 1, 2019 |
| Published in Issue | Year 2019 Volume: 4 Issue: 1 |
