Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Abstract

Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

Bioprocess Control: Current Progress and Future Perspectives

“…In numerous studies, predetermined exponential feed profiles (calculated based on initial conditions and growth kinetic parameters) are reported [33,34]. In recent studies, optimization of fed-batch operating conditions based on open loop strategy have been reported for recombinant lipase B, vaccine synthesis, ethanol, and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) production [34][35][36][37][38].…”

A Wavelet-Based Optimization Method for Biofuel Production

“…In this part, the colour histogram, derived of wavelet analysis of different reactions, is analysed. Table 3 shows the colour histogram of the two successful chemical reactions ( 1) and (2). The reaction result (1) has a bright colour and single colour component, while (2) with the double colour components.…”

“…Table 3 shows the colour histogram of the two successful chemical reactions ( 1) and (2). The reaction result (1) has a bright colour and single colour component, while (2) with the double colour components. This shows that the result (2) contains some impurities (mucilages), while the result (1) shows a biodiesel in which there are no impurities, having a homogeneous colour.…”

“…In the last years, awareness about energetic and environmental problems has encouraged many researchers to investigate the possibility of using alternative fuels instead of petrol and its derivatives [1]. Biofuels derived from biomass, such as bioethanol [2], biodiesel and biomethane, are a viable alternative. Biofuels are mostly derived from edible oils, nonedible oils, fats, waste cooking oil, and algae [3,4].…”

Real‐time optimization and control for polyhydroxybutyrate fed‐batch production at pilot plant scale

“…Such an approach was able to drive the system states to the desired set-points to maximize the biomass production, by considering the substrate concentration as the only available on-line measurement. In the work by De Freitas et al 28 the determination of the optimal control scheme for a fed-batch bio-ethanol process using evolutionary computation techniques is addressed. Results of this in silico study have shown once again that meta-heuristic algorithms (e.g.…”