Chemical Engineering Science, Vol.84, 619-627, 2012
Lipid production optimization and optimal control of heterotrophic microalgae fed-batch bioreactor
Interior point optimization and model predictive control along with moving horizon estimator are used to maximize and regulate lipid production in a fed-batch heterotrophic microalgae cultivation of Auxenochlorella protothecoides. Motivation for microalgae bioreactor optimal control arises from the need to increase lipid production rate, which results in economic feasibility of microalgae bio-fuel production process. A complex time-varying microalgae fed-batch growth and lipid production model (De la Hoz Siegler et al., 2011) is used and a large-scale nonlinear programming optimization along with moving horizon estimator and model predictive control are applied to maximize the lipid concentration in the bioreactor. An optimal feeding strategy for lipid production is determined using the state-of-the-art interior point optimizer (IPOPT) solver. Moving horizon estimator (MHE) and model predictive controller (MPC) are used to estimate unmeasurable state (nitrogen concentration) and provide regulation of a highly nonlinear and time-varying microalgae growth process as a realizable real-time control strategy. In addition to the constrained large-scale optimization, naturally present input constraints (lower and upper bound on feed rates) and state constraints (lower bound on all concentration related states and upper bound on glucose concentration) are accounted for in an explicit manner with moving horizon estimator and model predictive controller. The estimator and controller design is based on a set of linearized models in microalgae growth fed-batch process. The paper provides a reliable and computationally efficient optimization, estimation and regulation procedure suitable for the real-time microalgae bioreactor operation. The procedure takes into account present constraints on inputs and states, and measurement noises present in the realistic operation conditions and is computationally efficient, along with the improvement in results with respect to previous methods. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.