×
Gurubel, Kelly J.; Sanchez, Edgar N.; Coronado-Mendoza, Alberto; Zuniga-Grajeda, Virgilio; Sulbaran-Rangel, Belkis; Breton-Deval, Luz

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production. (English) Zbl 1425.92071

Optim. Control Appl. Methods 40, No. 5, 848-858 (2019).
Summary: This paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high-order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the \(\text{H}_2\) and \(\text{CH}_4\) productions along desired trajectories even in presence of disturbances.

MSC:

92C40 Biochemistry, molecular biology
49N90 Applications of optimal control and differential games
93C10 Nonlinear systems in control theory
93D20 Asymptotic stability in control theory

Keywords:

anaerobic bioprocesses; biofuels; inverse optimal control; passivity; recurrent neural network
Cite Review PDF
Full Text: DOI

References:

[1] RuggeriB, TommasiT, SanfilippoS. BioH2 & BioCH4 Through Anaerobic Digestion: From Research to Full‐Scale Applications. New York, NY: Springer; 2015.
[2] HussainA, ArifSM, AslamM. Emerging renewable and sustainable energy technologies: state of the art. Renew Sustain Energy Rev. 2017;71:12‐28.
[3] SilvaFM, MahlerCF, OliveiraLB, BassinJP. Hydrogen and methane production in a two‐stage anaerobic digestion system by co‐digestion of food waste, sewage sludge and glycerol. Waste Management. 2018;76:339‐349.
[4] LavagnoloMC, GirottoF, RafieeniaR, DanieliL, AlibardiL. Two‐stage anaerobic digestion of the organic fraction of municipal solid waste-effects of process conditions during batch tests. Renewable Energy. 2018;126:14‐20.
[5] FuSF, XuXH, DaiM, YuanXZ, GuoRB. Hydrogen and methane production from vinasse using two‐stage anaerobic digestion. Process Saf Environ Prot. 2017;107:81‐86.
[6] DuanZ, BournazouMNC, KravarisC. Dynamic model reduction for two‐stage anaerobic digestion processes. Chem Eng J. 2017;327:1102‐1116.
[7] DiopS, ChorukovaE, SimeonovI. Modeling and specific growth rates estimation of a two‐stage anaerobic digestion process for hydrogen and methane production. IFAC‐PapersOnline. 2017;50(1):12641‐12646.
[8] BlumensaatF, KellerJ. Modelling of two‐stage anaerobic digestion using the IWA anaerobic digestion model no. 1 (ADM1). Water Research. 2005;39(1):171‐183.
[9] GhatakMD, GhatakA. Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co‐substrates. Fuel. 2018;232:178‐189.
[10] NairVV, DharH, KumarS, ThallaAK, MukherjeeS, WongJW. Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory‐scale anaerobic bioreactor. Bioresource Technology. 2016;217:90‐99.
[11] JhaP, KanaEBG, SchmidtS. Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor?Int J Hydrog Energy. 2017;42(30):18875‐18883.
[12] HaddonA, RamirezH, RapaportA. First results of optimal control of average biogas production for the chemostat over an infinite horizon. Paper presented at: IFAC International Conference on Mathematical Modelling‐MATHMOD; 2018; Vienna, Austria.
[13] Méndez‐AcostaHO, Campos‐RodríguezA, González‐ÁlvarezV, García‐SandovalJP, Snell‐CastroR, LatrilleE. A hybrid cascade control scheme for the VFA and COD regulation in two‐stage anaerobic digestion processes. Bioresource Technology. 2016;218:1195‐1202.
[14] Hernandez‐MejiaG, AlanisAY, Hernandez‐VargasEA. Neural inverse optimal control for discrete‐time impulsive systems. Neurocomputing. 2018;314:101‐108.
[15] El‐HussienyH, AbouelsoudAA, AssalSF, MegahedSM. Adaptive learning of human motor behaviors: an evolving inverse optimal control approach. Eng Appl Artif Intell. 2016;50:115‐124.
[16] BorisovM, DimitrovaN, SimeonovI. Mathematical modelling of anaerobic digestion with hydrogen and methane production. IFAC‐PapersOnline. 2016;49(26):231‐238.
[17] CamberosSA, GurubelKJ, SanchezEN, AguirreSA, PerezRG. Neuronal modeling of a two stages anaerobic digestion process for biofuels production. IFAC‐PapersOnline. 2018;51(13):408‐413.
[18] CaoW, WangX, MingZ, GaoJ. A review on neural networks with random weights. Neurocomputing. 2018;275:278‐287.
[19] AsgariH, ChenX, MoriniM, et al. NARX models for simulation of the start‐up operation of a single‐shaft gas turbine. Appl Therm Eng. 2016;93:368‐376.
[20] SanchezEN, AlanísAY, LoukianovAG. Discrete‐Time High Order Neural Control: Trained With Kalman Filtering. Vol. 112. Cham, Switzerland: Springer; 2008. · Zbl 1204.93004
[21] AlanisAY, SanchezEN, LoukianovAG, HernandezEA. Discrete‐time recurrent high order neural networks for nonlinear identification. J Frankl Inst. 2010;347(7):1253‐1265. · Zbl 1202.93082
[22] HaykinS. Kalman Filtering and Neural Networks. New York, NY: John Wiley & Sons; 2004.
[23] SanchezEN, Ornelas‐TellezF. Discrete‐Time Inverse Optimal Control for Nonlinear Systems. Boca Raton, FL: CRC Press; 2017.
[24] LeonBS, AlanisAY, SanchezEN, Ruiz‐VelazquezE, Ornelas‐TellezF. Inverse optimal neural control for a class of discrete‐time nonlinear positive systems. Int J Adapt Control. 2012;26(7):614‐629. · Zbl 1250.49036
[25] LinW, ByrnesCI. Design of discrete‐time nonlinear control systems via smooth feedback. IEEE Trans Autom Control. 1994;39(11):2340‐2346. · Zbl 0825.93424
[26] SimeonovIS, ChorukovaEI, LakovVT. Modelling and extremum seeking control of two‐stage biotechnological process. IFAC‐PaperOnline. 2018;51(30):677‐682.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.