Parametric, Optimization-Based Study on the Feasibility of a Multisegment Antisolvent Crystallizer for in Situ Fines Removal and Matching of Target Size Distribution

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Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Multiobjective Optimization to Optimal Moroccan Diet Using Genetic Algorithm

Proper glucose control is designed to prevent or delay the complications of diabetes. Various contexts can lead to a fluctuation of the blood sugar level to a greater or lesser extent. It can be, for example, eating habits, treatment, intense physical activity, etc. The feeding problem interpolated by a minimum cost function is well-known in the literature. The main goal of this paper is to introduce a multiobjective programming model with constraints for the diet problem with two objective functions, the first of which is the total glycemic load of the diet while the second objective function is the cost of the diet. the MOGA (multiobjective Genetic Algorithm) algorithm was used to resolve the proposed model. The experimental results show that our system ([proposed model – MOGA]) is able to produce adequate diets that can settle glycemic load and cost while respecting the patient\u27s requirements