Biological control is the artificial manipulation of natural enemies of a pest for its regulation to densities below a threshold for economic damage. The authors address the biological control of a class of pest population models using a model‐based robust feedback approach. The proposed control framework is based on a recursive cascade control scheme exploiting the chained form of pest population models and the use of virtual inputs. The robust feedback is formulated considering the non‐linear model uncertainties via a simple and intuitive control design. Numerical results on three pest biological control problems show that the proposed model‐based robust feedback can regulate the pest population at the desired reference via the manipulation of a biological control action despite model uncertainties.

Living cells, type of substrate, enzymatic hydrolysis play an important role in the efficiency of ethanol production; however, the kinetic parameters of biochemical reactions necessary for modelling these processes are often not accessible directly through experiments. In this context, for the implementation of suitable operational strategies, it is necessary to have kinetic models able to describe the process as realistically as possible. This paper proposes a comparative study of two nonlinear techniques for parametric identification of a kinetic model for ethanol production from recycled paper sludge in order to improve process performance. The parameters of the model are optimized by two methods: using the Levenberg–Marquardt optimization approach and Genetic Algorithms. The performances of both techniques are evaluated using a numerical simulation. The optimal value of these parameters have been obtained based on Genetic Algorithm. Finally, the effect of parametric adjustment and dilution rate on productivity was demonstrated by changing the batch operation to the continuous operating model. The maximum ethanol concentration was about 13.25 g/l in batch process and about 13.9 g/l at Dilution rate: 0.005 1/h corresponding to a productivity of 0.327 in continuous process.

During the anaerobic digestion (AD) of fruit and vegetable waste (FVW), excessive particle size reduction can lead to the overproduction and inhibition of methanogenic microorganisms. This paper presents an in-depth analysis through experimental assays, modeling, and response surface analysis of the effect of particle size on methane production. A simple model was proposed considering the inhibition of the growth of methanogenic microorganisms and surface-based hydrolysis kinetics. The model parameters were estimated using experimental data from batch systems fed with FVW of varying particle sizes (ranging from 1.8 to 1000 μm). Response surface methodology establishes a statistical model for estimating methane production based on particle size and concentration. Numerical and statistical analyses were conducted using Matlab R2024a and Minitab 24 software. A model with an R² of 0.89 was obtained, which determined an optimal concentration of 8.2 $\mathrm{k}\mathrm{g}·{\mathrm{m}}^{-3}$ and a particle size of 742.3 $\mathsf{\mu }\mathrm{m}$, yielding a methane production of 303.3 ${\mathrm{m}}^3·\mathrm{k}{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$, similar to the experimentally obtained range of 300.95 to 316.7 ${\mathrm{m}}^3·\mathrm{k}{\mathrm{g}}^{-1} \mathrm{V}\mathrm{S}$.