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.

Fault detection and diagnosis is important in ensuring the stability and safety of chemical processes. However, limited studies have focused on strong periodic disturbance and non-Gaussian process monitoring. By utilizing the data-driven monitoring method, we have proposed the residual analysis independent component analysis based on average multivariate cumulative sum (AMRA-ICA) method to avoid the influence of periodic disturbance in non-Gaussian chemical processes with periodic disturbance. Average multivariate cumulative sum (AM) is introduced in the AMRA-ICA method for disturbance cycle synchronization. Residual analysis (RA) is employed to remove the disturbance in the data set and to obtain the normal residual. The independent component analysis (ICA) method is then utilized to monitor the residual, and an improved contribution histogram method is proposed to identify the cause of the fault. The proposed method has been applied to the classic benchmark Tennessee Eastman process with and without periodic disturbance and to an ethylene compressor which is periodically affected by ambient temperature. Simulation results illustrate that the proposed AMRA-ICA method could solve the monitoring problem of non-Gaussian processes with periodic disturbance more effectively and accurately compared with the residual analysis PCA (RA-PCA) and the local tangent space alignment-ICA (LTSA-ICA). The AMRA-ICA method can also manage conventional processes without periodic disturbance.

Dynamic optimization problems (DOP) in chemical processes are very challenging because of their highly nonlinear, multidimensional, multipeak and constrained nature. In this paper, we propose a novel algorithm named hybrid gradient particle swarm optimization (HGPSO) by hybridizing particle swarm optimization (PSO) with gradient‐based algorithms (GBA). HGSPO can improve the convergence rate and solution precision of pure PSO, and avoid getting trapped to local optimums with pure GBA search. We further incorporate HGPSO into control vector parameterization (CVP), a method converting DOP into nonlinear programming, to solve five complex DOPs. These DOPs include multimodal, multidimensional and constrained problems. The experiments demonstrate that HGPSO performs much better in terms of solution precision and computational cost when compared with other PSO variants.

A full cycle of an industrial ethylene dichloride cracker is simulated. Given the intense heat coupling between the furnace and the reactor, the cracker is divided into two parts: the furnace model and the reactor model, with heat flux and flue gas temperature profiles connecting the two models. A radical mechanism with coke formation is adopted to describe the EDC cracking reactions with 24 reaction equations and 31 components. In the full cycle simulation, two important aspects, namely, CCl4 concentration and fuel gas allocation, are investigated to understand the overall benefits of the whole operation cycle. Addition of the promoter CCl4 to EDC raw material can improve EDC conversion. However, this process aggravates the coking reaction, which causes the sharp deterioration of the cracking performance and the shortening of the running cycle. On the other hand, the fuel gas allocation factor facilitates analysis of the fuel gas allocation strategies. Increasing the fuel gas amount at the furnace bottom can effectively improve the heat transfer efficiency of the EDC cracker. In particular, this process enhances heat transfer at the end of the tubular reactor, which improves the EDC conversion. However, coke deposition greatly shortens the run cycle. A comprehensive analysis shows that the concentration of the CCl4 promoter should be controlled at 100 ppm wt % and the fuel gas allocation factor should be maintained at 0.36 to guarantee the overall economic benefits of the EDC cracker in the full operation cycle.

This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.

Dynamic optimization problems in chemical processes are often quite challenging because these problems often involve multiple and conflicting objectives. To solve the multi-objective dynamic optimization problems (MDOPs), in this paper, we propose a new multi-objective differential evolution (MODE) variant, named MODE-RMO for short, inspired by the phenomenon that good individuals which contain good information often have more chance to be utilized to guide other individuals. In MODE-RMO, the ranking-based mutation operator is integrated into the MODE algorithm to accelerate the convergence speed, and thus enhance the performance. The performance of our proposed algorithm is firstly evaluated in ten test functions and compared with other MOEAs. The results demonstrate that MODE-RMO can generate Pareto optimal fronts with satisfactory convergence and diversity. Finally, MODE-RMO is applied to solve three MDOPs taken from literature using control vector parameterization. The obtained results indicate that MODE-RMO is an effective and efficient approach for MDOPs.