Here we present a new hybrid genetic algorithm (HGA) with the Baldwin effect. In the HGA, a local search is employed to change the fitness of individuals but the acquired improvements do not change the individual itself. This local search step exploits the Baldwin effect. Some numerical applications show that this algorithm can yield the global optimum more efficiently than commonly used HGAs. A theorem is presented that guarantees the convergence in probability of the new HGA.

The oxidation of p-xylene to terephthalic acid (TA) is a significant chemical process of purified TA production. Variation of operation conditions of the reaction directly affects the quality of TA. In this paper a fuzzy adaptive immune algorithm (FAIA) was proposed to realize the operation condition optimization of the p-xylene oxidation reaction. In FAIA, a chaotic hypermutation was developed to strengthen the searching ability of the algorithm, and a new immune network regulatory strategy was devised to maintain population adversity. Furthermore, two fuzzy logic modules were constructed to adjust parameters, further increasing the adaptability of the algorithm. The data of function optimization show that FAIA can quickly converge to the global optimum and overcome premature problems. Optimization results of process variable values of the p-xylene oxidation reaction indicate that the application of FAIA in an oxidation reaction process can greatly save the time of operation condition selection and reduce production cost.

A novel kinetic model based on the free radical mechanism is used to simulate the oxidation of p-xylene (PX) in a continuous stirred-tank reactor (CSTR) under industrial operating conditions. Because this kinetic model cannot provide appropriate prediction of the influence of the reaction factors, such as catalyst concentrations, water concentrations, and temperatures, on the kinetic parameters for oxidation of PX in the laboratory semibatch reactor (SBR), the kinetic parameters that are highly nonlinear of the reaction factors are estimated by a back-propagation neural network (BPNN). Furthermore, correction coefficients are introduced to accurately evaluate the kinetic parameters based on Adaptive Immune Genetic Algorithm (AIGA) due to the significant difference between the nature of PX oxidation conducted in the laboratory SBR and in the industrial CSTR. The model with the evaluated optimum kinetic parameters is obtained, and its efficiency is validated via comparison with industrial data.

Considering that the traditional method for controller performance assessment is mainly based on minimum variance, which is an unattainable boundary, a new vector-based approach is proposed that is driven by the historical data and is not very dependent on prior knowledge of the process unit. A performance vector is designed to represent the controller precision and its response speed. The length and angle of the vector are constructed using statistical theory and signal processing techniques, which is the key step in the proposed method. It works well for disturbance rejection situation in single-input–single-output (SISO) systems. In particular, an index Pf is introduced as a measurement of the controller performance to show how much potential the control loop will still have for improvement. The Wood–Berry model is used for simulation studies to demonstrate the effectiveness of the proposed method; then, the possibility of extending the method to a multiple-input–multiple-output (MIMO) system is also discussed here.

Purified terephthalic acid (PTA) is used for producing a variety of polyesters. In the production of PTA, p-xylene (PX) is first transformed into terephthalic acid (TA) by oxidation process and then TA is refined. As a key step, the oxidation of PX to TA is a significant chemical process of PTA production. To improve qualified product yield with low energy consumption, in this paper, multi-objective optimization of various conflicting objectives (namely minimization of combustion loss, maximization of TA yield) is conducted using self-adaptive multi-objective differential evolution algorithm (SADE). The main characteristic of it is that DE's trial vector generation strategies and the corresponding control parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, to handle constraints in multi-objective problems, the pseudo feasible concept is proposed to effectively utilize the critical information carried by some infeasible solutions. Optimization results of PX oxidation reaction process indicate that application of SADE can greatly improve the yield of TA with low combustion loss without degenerating TA quality. Furthermore, SADE can provide a set of Pareto optimal solutions and then suitable multi-criterion decision-making techniques can be employed to select one or a small set of the optimal solution(s) of design parameter(s) based on preference.