Feedforward Artificial Neural Networks-Model Prediction Control (FANN-MPC) for Semi-Simultaneous Saccharification and Fermentation (SSSF) Bioethanol Process
Keywords:
Process Control, Model Predictive Control, Bioethanol, Nonlinear Model Predictive Control, Semi-simultaneous saccharification and fermentation (SSSF)Abstract
The main focus of this work is on the development of nonlinear model-based control for the production of bioethanol from lignocellulosic materials following the semi-simultaneous saccharification and fermentation (SSSF) process. It is observed from the current study that the SSSF process is able to give higher yield and higher ethanol concentration in comparison with the simultaneous saccharification and fermentation (SSF) process, and the separate hydrolysis and fermentation (SHF) process. The SSSF process in this study, which is referred to as the SSSF 24, includes 24 hours of pre-hydrolytic phase, and 48 hours of SSF phase. Mechanistic models for the fed-batch operating mode of SSSF 24 were initially developed to represent the actual dynamics of this fermentation process. Feedforward Artificial Neural Network (FANN) model was developed in nonlinear model predictive control (NMPC) to improve the performance of the controller. Data for the training, testing and validation procedure to construct the FANN were generated through randomization of the input cellulose concentration and dilution rate using the mechanistic models. The total sum of squared errors (SSE) for training and testing, and coefficient of determination, R2, value for fed-batch operating mode are 0.0508 and 0.9998, respectively, for their best FANN architecture of 2 – 15 – 1 with 15 hidden neurons. The FANN-model predictive control (NMPC) strategy developed for the fed-batch operating mode of SSSF 24 has good setpoint tracking ability and is robust
References
Mason, J. E. World energy analysis: H2 now or later? Energy Econ. 2007, 35, 1315-1329.
Leblond, D. IEA: fossil energy to dominate market through 2030. Oil & Gas J. 2006, 104, 28-29.
EIA. Annual Energy Outlook 2021. Washington: Energy Information Administration 2021. Available online: https://www.eia.gov/outlooks/aeo/ (accessed on 01/12/2021).
Rosegrant, M. W.; Msangi, S.; Sulser, T.; Valmonte-Santos, R.; Hazell, P.; & Pachauri, R. K. Biofuels and the global food balance: bioenergy and agriculture promises and challenges. International Food Policy Research Institute (IFPRI) Focus 14 2006.
Abbasi, T.; Abbasi, S. A. Biomass energy and the environmental impacts associated with its production and utilization. Renew. Sust. Energ. Rev. 2010, 14, 919-937.
Osarhiemhen, A.; Augustine, O. A.; Oluranti, A; Francis, B.E. A review on the sustainable energy generation from the pyrolysis of coconut biomass. Scientific African 2021, 13, e00909.
Muradov, N. Z.; Veziroglu, T. N. “Green" path from fossil-based to hydrogen economy: An overview of carbon-neutral technologies. Int. J. Hydrog. Energy 2008, 33, 6804-6839.
Karpan, B.; Abdul Raman, A. A.; Taieb Aroua, M. K. Waste-to-energy: Coal-like refuse derived fuel from hazardous waste and biomass mixture. Process Saf Environ Prot. 2021 149, 655-664.
Gajalakshmi, S.; Abbasi, S. A. Solid waste management by composting: state of the art. Crit Rev Environ Sci Technol. 2008, 38, 311-400.
Volk, T. A.; Abrahamson, L. P.; Nowak, C. A.; Smart, L. B.; Tharakan, P. J.; White, E. H. The development of short-rotation willow in the northeastern United States for bioenergy and bioproducts, agroforestry and phytoremediation. Biomass Bioenergy 2006, 30, 715-727.
Tuskan, G. A.; Difazio, S.; Jansson, S.; Bohlmann, J.; Grigoriev, I.; Hellsten, U. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 2006, 313, 1596-1604.
Geyer, W. A. Biomass production in the Central Great Plains USA under various coppice regimes. Biomass Bioenergy 2006, 30, 778-783.
Parrish, D. J.; Fike, J. H. The biology and agronomy of switchgrass for biofuels. Crit Rev Plant Sci. 2005, 23, 423-459.
Hallam, A.; Anderson, I. C.; Buxton, D. R. Comparative economic analysis of perennial, annual, and intercrops for biomass production. Biomass Bioenergy 2001, 21, 407-424.
Lewandowski, I.; Scurlock, J. M. O.; Lindvall, E.; Christou, M. The development and current status of perennial rhizomatous grasses as energy crops in the US and Europe. Biomass Bioenergy 2003, 25, 335-361.
Heaton, E.; Voight, T.; Long, S. P. A quantitive review comparing the yields of two candidate C4 biomass crops in relation to nitrogen, temperature and water. Biomass Bioenergy 2004, 27, 21-30.
Flevaris, K.; Chatzidoukas, C. Optimal fed-batch bioreactor operating strategies for the microbial production of lignocellulosic bioethanol and exploration of their economic implications: A step forward towards sustainability and commercialization. J. Clean. Prod. 2021, 295, 126384.
Devi, A.; Singh, A.; Bajar, S.; Pant, D.; Ud Din, Z. Ethanol from lignocellulosic biomass: An in-depth analysis of pre-treatment methods, fermentation approaches and detoxification processes. J. Environ. Chem. Eng. 2021, 9(5), 105798.
Haeun, Y.; Ha, E.B.; Dongho, H.; Jay, H.L. Reinforcement learning for batch process control: Review and perspectives. Annu Rev Control 2021, in press.
Feng, X.; Yu, T.; Wang, J. L. Nonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor. Chin. J. Chem. Eng. 2021, 20 (5), 988-994.
Petre, E.; Selişteanu, D.; Roman, M. Advanced nonlinear control strategies for a fermentation bioreactor used for ethanol production. Bioresour. Technol. 2021 328, 124836.
Shen, J.; Agblevor, F. A. Modelling semi-simultaneous saccharification and fermentation of ethanol production from cellulose. Biomass Bioenergy 2010, 34(8), 1098-1107.
Meleiro, L. A.C.; Zuben, F. J. V.; Filho, R. M. Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process. Eng. Appl. Artif. Intell. 2009, 22, 201-215.
Ahmad, Z.; Zhang, J. Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process modelling. Comput. Chem. Eng. 2005, 30, 295-308.
Jayalakshmi, T.; Santhakumaran, A. Statistical normalization and backpropagation for classification. International Journal of Computer Theory and Engineering 2011, 3(1), 1793-8201.
Lera, G.; Pinzolas, M. Neighbourhood-based Levenberg-Marquardt algorithm for neural network training. IEEE Transactions on Neural Networks 2002, 13(5), 1200-1203.
Nagy, Z. K. Model-based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chem. Eng. J. 2007, 127, 95-109.
The MathWorks, I. NN Predictive Control.
Vasickaninova, A.; Bakosova, M.; Meszaros, A.; Klemes, J. J. Neural network predictive control of a heat exchanger. Appl. Therm. Eng. 2011, 1-7.
Zhang, J.; Morris, A. J. A sequential learning approach for single hidden layer neural networks. Neural Netw. 1998, 11(1), 65-80.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 ASEAN Journal of Process Control
This work is licensed under a Creative Commons Attribution 4.0 International License.
