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A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP) problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm

In many continuous fermentation processes, the control objective is to maximize productivity per unit time. The optimum operational point in the steady state can be obtained by maximizing the productivity rate using feed substrate concentration as the independent variable with the equations of the static model as constraints. In the present study, three model-based control schemes have been developed and implemented for a continuous fermenter. The first method modifies the well-known dynamic matrix control (DMC) algorithm by making it adaptive. The other two use nonlinear model predictive control algorithms (NMPC, nonlinear model predictive control) for calculation of control actions. The NMPC1 algorithm, which uses orthogonal collocation in finite elements, acted similar to NMPC2, which uses equidistant collocation. These algorithms are compared with DMC. The results obtained show the good performance of nonlinear algorithms

Manufacturing plants are increasingly complex and integrated, requiring control systems able to identify the interactions between the various operating units. Production planning and control design of a process are tools that, if combined, bring many economic benefits to the processes since they aim to identify and maintain optimal decision operations to a system. This work uses such integration between production planning and plantwide control to propose a control system for the Williams-Otto plant from the definition of the operating optimal point for coordinated decentralized optimization, in which the original optimization problem decomposition into smaller coordinated problems ensure that the found local optimum also meets the requirements of the global system. The results for decentralized optimization are satisfactory and very similar to the global optimum problem and to the control system response proposed based on the optimal obtained. It is effective taking smooth actions, working with (economic) optimal set points (economically) of operation. The unification of production planning techniques and plantwide control techniques is an effective tool for the control system design for entire plants.

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A modeling study was completed to develop a methodology that combines the sequencing and finite difference methods for the simulation of a heterogeneous model of a tubular reactor applied in the treatment of wastewater. The system included a liquid phase (convection–diffusion–transport) and a solid phase (diffusion reaction) that was obtained by completing a mass balance in the reactor and in the particle, respectively. The model was solved using a pilot-scale horizontal-flow anaerobic immobilized biomass (HAIB) reactor to treat domestic sewage, with the concentration results compared with the experimental data. A comparison of the behavior of the liquid phase concentration profile and the experimental results indicated that both the numerical methods offer a good description of the behavior of the concentration along the reactor. The advantage of the sequencing method over the finite difference method is that it is easier to apply and requires less computational time to model the dynamic simulation of outlet response of HAIB.

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The major drawbacks in large-scale solid-state fermentation processes are related to difficulty in controlling the medium temperature and moisture content, which are variables that directly affect microbial growth and product formation. Several mathematical models have been developed to describe these effects, although none has simultaneously considered distinct growth phases, growth restrictions caused by large temperature variations at several distinct moisture content conditions, and product formation pathways. In this manner, the objectives of this paper were to develop a mathematical model to represent the process under different operational conditions and a model-based optimization procedure to investigate the effects of varying temperature profiles to maximize a (hemi) cellulolytic enzyme production during cultivation of Aspergillus niger under solid state fermentation. The proposed model correlates fungal growth with the CO 2 production rates and with enzymatic production by the Luedeking-Piret function. It was developed with data acquired in a laboratory-scale column-type bioreactor in controlled conditions of aeration, temperature, and inlet air relative humidity. The developed model accurately predicted the respiration profile responses at all temperatures, under the most productive moisture content conditions. Incubation of the culture with the optimized temperature profile improved the enzymatic production, compared to the estimated optimum static temperature. These findings demonstrate the usefulness of this model for the optimization of larger-scale SSF processes.