Advanced nonlinear control strategies for a fermentation bioreactor used for ethanol production

Petre, Emil; Selişteanu, Dan; Roman, Monica

doi:10.1016/j.biortech.2021.124836

Cited by 24 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…with X X X P P P (24) and when the initial states X0 and P0 approach zero, ψ and Sin are identical. From the state estimation perspective, the varying of Sin because of upstream disturbance could be reflected by changes in the time-derivative of ψ, and one can adopt observer-based estimation (OBE) on Sin through ψ.…”

Section: Estimation Of the Input Substrate Concentrationmentioning

confidence: 99%

“…Firstly, ψ = S + P/Y XP + X/Y XS is constructed which gives dψ/dt = D(−ψ + S in ). Clearly, the auxiliary variable ψ is highly correlated with S in through mass balance, S in ≡ S + δX/Y XS + δP/Y XP with : δX = X − X 0 , δP = P − P 0 (24) and when the initial states X 0 and P 0 approach zero, ψ and S in are identical. From the state estimation perspective, the varying of S in because of upstream disturbance could be reflected by changes in the time-derivative of ψ, and one can adopt observer-based estimation (OBE) on S in through ψ.…”

Section: Estimation Of the Input Substrate Concentrationmentioning

confidence: 99%

“…When D = D c , (X * , P * ) degenerates to the washout condition. Substituting F(P) to system (24), one has,…”

Section: Analysis Of the Open-loop Systemmentioning

confidence: 99%

“…The estimation of the input substrate concentration is more difficult because no direct one-on-one detectable signal could be mapped on S in , and we introduce an extra variable ψ defined in Equation (24) to represent S in . The underlying idea follows principle of substrate conservation; hence, for the detection of ψ, all three fermentation related states (S, X, P) are required, from measurement instruments or by estimation.…”

Section: Numerical Simulation On the Closed-loop Systemmentioning

confidence: 99%

“…The research in this domain is oriented both to the developing of suitable mathematical models and to the design of appropriate monitoring and control strategies able to assure the stability. To summarize, the ethanol fermentation process is influenced by a number of parameters, such as temperature, substrate, pH value, fermentation time, mixing speed, and inoculum size, as well as the concentration of ethanol in the system [24]. Hence, the system to control is multidimensional and complex, and their models contain kinetic parameters that are uncertain and time-varying.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Qin,

Zhai

2024

Processes

View full text Add to dashboard Cite

Traditional batch ethanol fermentation poses the problems of poor production and economic viability because the lag and stationary phase always demand considerable fermentation time; plus, downtime between batches is requested to harvest, clean, and sterilize, decreasing the overall productivity and increasing labor cost. To promote productivity and prolong the production period, avoid process instability, and assure a substantial production of ethanol and a minimal quantity of residual substrate, this paper proposed a nonlinear adaptive control which can realize global stabilizing control of the process starting from batch mode to achieve batch/washout avoidance. Due to the dynamic nature and complexity of the process, novel estimation and control schemes are designed and tested on an ethanol fermentation model. These schemes are global stabilizing control laws including adaptive control to avoid input saturation, nonlinear estimation of the unknown influential concentration through a higher-order sliding mode observer, and state observers and parameter estimators used to estimate the unknown states and kinetics. Since the temperature is an important factor for an efficient operation of the process, a split ranging control framework is also developed. To verify the process performance improvement by continuous fermentation, tests performed via numerical simulations under realistic conditions are presented.

show abstract

Section: Estimation Of the Input Substrate Concentrationmentioning

confidence: 99%

Section: Estimation Of the Input Substrate Concentrationmentioning

confidence: 99%

“…When D = D c , (X * , P * ) degenerates to the washout condition. Substituting F(P) to system (24), one has,…”

Section: Analysis Of the Open-loop Systemmentioning

confidence: 99%

Section: Numerical Simulation On the Closed-loop Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Qin,

Zhai

2024

Processes

View full text Add to dashboard Cite

show abstract

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production

Rajasekhar,

Radhakrishnan,

Mohamed

2024

Biotech & Bioengineering

View full text Add to dashboard Cite

Ethanol production is a significant industrial bioprocess for energy. The primary objective of this study is to control the process reactor temperature to get the desired product, that is, ethanol. Advanced model‐based control systems face challenges due to model‐process mismatch, but Reinforcement Learning (RL) is a class of machine learning which can help by allowing agents to learn policies directly from the environment. Hence a RL algorithm called twin delayed deep deterministic policy gradient (TD3) is employed. The control of reactor temperature is categorized into two categories namely unconstrained and constrained control approaches. The TD3 with various reward functions are tested on a nonlinear bioreactor model. The results are compared with existing popular RL algorithm, namely, deep deterministic policy gradient (DDPG) algorithm with a performance measure such as mean squared error (MSE). In the unconstrained control of the bioreactor, the TD3 based controller designed with the integral absolute error (IAE) reward yields a lower MSE of 0.22, whereas the DDPG produces an MSE of 0.29. Similarly, in the case of constrained controller, TD3 based controller designed with the IAE reward yields a lower MSE of 0.38, whereas DDPG produces an MSE of 0.48. In addition, the TD3 trained agent successfully rejects the disturbances, namely, input flow rate and inlet temperature in addition to a setpoint change with better performance metrics.

show abstract

Microbial bioethanol fermentation technologies—Recent trends and future prospects

Behera

Saranraj

Ray³

2022

Biofuels and Biorefining

View full text Add to dashboard Cite

Advanced nonlinear control strategies for a fermentation bioreactor used for ethanol production

Cited by 24 publications

References 18 publications

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Global Stabilizing Control of a Continuous Ethanol Fermentation Process Starting from Batch Mode Production

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production

Microbial bioethanol fermentation technologies—Recent trends and future prospects

Contact Info

Product

Resources

About