TY - GEN
T1 - Optimization of Biogas Plant Operational Efficiencies Using Evolutionary Algorithms
AU - Ighravwe, Desmond Eseoghene
AU - Mashao, Daniel
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - The growing needs for clean energy will continue to attract global attention, especially as it has been recognized as a means of managing solid wastes - especially from households and industrial sector. We now have different waste-to-energy technologies for small and medium-scale plants. But sparse information exists on how to optimize these plants operational efficiencies, especially boilers and reformers. Hence, this article considers the optimization of these efficiencies to optimal electricity generation. This objective is achieved using a nonlinear programming approach. The proposed model utility was tested using a case study of six locations in Southwest Nigeria. A comparison of Genetic algorithm (GA) and Differential Evolution (DE) algorithm are presented as solution methods for the model. In terms of the total electricity generated, there is no significant difference between these algorithms results. The total electricity generated is 10MW, while the average boilers and reformers efficiencies are 0.9 and 0.8, respectively. To be strategic with a waste-to-energy operation, this article recommends that optimal parametric settings for a plant's operational efficiencies should be combined with experts' opinions.
AB - The growing needs for clean energy will continue to attract global attention, especially as it has been recognized as a means of managing solid wastes - especially from households and industrial sector. We now have different waste-to-energy technologies for small and medium-scale plants. But sparse information exists on how to optimize these plants operational efficiencies, especially boilers and reformers. Hence, this article considers the optimization of these efficiencies to optimal electricity generation. This objective is achieved using a nonlinear programming approach. The proposed model utility was tested using a case study of six locations in Southwest Nigeria. A comparison of Genetic algorithm (GA) and Differential Evolution (DE) algorithm are presented as solution methods for the model. In terms of the total electricity generated, there is no significant difference between these algorithms results. The total electricity generated is 10MW, while the average boilers and reformers efficiencies are 0.9 and 0.8, respectively. To be strategic with a waste-to-energy operation, this article recommends that optimal parametric settings for a plant's operational efficiencies should be combined with experts' opinions.
KW - evolutionary algorithm
KW - food waste
KW - hydrogen gas
KW - optimization model
UR - http://www.scopus.com/inward/record.url?scp=85081556257&partnerID=8YFLogxK
U2 - 10.1109/REPE48501.2019.9025127
DO - 10.1109/REPE48501.2019.9025127
M3 - Conference contribution
AN - SCOPUS:85081556257
T3 - 2019 IEEE 2nd International Conference on Renewable Energy and Power Engineering, REPE 2019
SP - 135
EP - 139
BT - 2019 IEEE 2nd International Conference on Renewable Energy and Power Engineering, REPE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Renewable Energy and Power Engineering, REPE 2019
Y2 - 2 November 2019 through 4 November 2019
ER -
