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Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design) points from a generally continuous design space at which the models deviate most from each other under the condition that they are best fitted to those points. Thus, the T-criterion represents a bi-level optimization problem, which can be transferred into a semi-infinite one but whose solution is very unstable or time consuming for non-linear models and non-convex lower- and upper-level problems. If one considers only a finite number of possible design points, a numerically well tractable linear semi-infinite optimization problem arises. Since this is only an approximation of the original model discrimination problem, we propose an algorithm which alternately and adaptively refines discretizations of the parameter as well as of the design space and, thus, solves a sequence of linear semi-infinite programs. We prove convergence of our method and its subroutine and show on the basis of discrimination tasks from process engineering that our approach is stable and can outperform the known methods. [ABSTRACT FROM AUTHOR]

In this era of industrial revolution and diminishing petroleum reserves, alternative options (woody biomass) for building next-generation fuels need to be critically explored. Here, process engineering aspects of 5-hydroxymethylfurfural (5-HMF) from Bambusa bamboo in ionic liquid ([Bmim]Cl) in the presence of a catalyst, CrCl3, in batch reactor have been explored in great detail. 5-HMF upon hydrogenation liberates 2,5-dimethylfuran (DMF), which is a liquid fuel. To maximize the production of platform chemical-5-HMF, effects of different mixing speeds (150–1200 rpm) and temperatures (120–160 °C) were captured on the yield of 5-HMF and glucose. A highest 5-HMF yield (25%) was obtained at 120 °C and 1200 rpm within a reaction time of 6 h. Moreover, a kinetic analysis of transformation of biomass into 5-HMF was carried out using curve fitting to estimate kinetic constants k1, k2, and k3. Mixing at asymptotic limits, i.e., no mixing (0 rpm) and very high mixing (1200 rpm), enables us to devise mixing regimes: 0–400 rpm, mass transfer–limited regime; 400–1100 rpm, intermediate regime; and > 1100 rpm, reaction-limited regime. Thus, tremendous improvement in reaction rate constants (k1, k2, k3) was observed when operated at higher mixing conditions (reaction-limited regime). Mixing limitation for this IL-based system can be eliminated by operating the reactor above 800 rpm, whereas lower temperature restricts conversion of 5-HMF to LA and FA. Hence, higher mixing speeds, i.e., > 1100 rpm, and lower temperature, i.e., ≈ 120 °C, can be suggested as the optimum operating conditions for maximization of platform chemical-5-HMF in the catalytic conversion of Bambusa bamboo in batch reactor. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]