Qunxiong Zhu

A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization

This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new single-objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems. This novel algorithm makes the best of ergodicity of PWLCM to help PSO with the global search while employing the SQP to accelerate the local search. Five unconstrained benchmarks, eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach. The simulation results compared with other state-of-art methods demonstrate the effectiveness and robustness of the proposed RHPSO for both unconstrained and constrained problems of different dimensions.

Energy efficiency analysis based on DEA integrated ISM

The petrochemical industry evaluation is affected by numerous factors. Many previous studies proposed a use of data envelopment analysis (DEA) as a methodology for energy efficiency analysis in the petrochemical industry. However, excessive decision-making units (DMUs) of DEA model result in difficulties in evaluation and comparison of the different DMUs. In this paper, a new energy analysis framework of petrochemical industrial processes based on DEA integrated interpretative structural model (ISM) is proposed. The ISM method is brought up based on the partial correlation coefficient method to find the main factors and basic reasons that affect the energy consumption of the ethylene production system, which serve as the inputs of the DEA. Meanwhile, ethylene, propylene and C4 productions of the ethylene production system sever as the outputs of the DEA. Then the fractional DEA model is solved by using the linear programming method. The proposed evaluation method can overcome the shortcomings of the DEA model mentioned above, and also is able to reflect the effectiveness of the DMUs and guide the improvement directions of the ineffective DMUs based on slack variables. Our approach is applied in the energy efficiency analysis of Chinese ethylene industry in the petrochemical field. The empirical results show that the proposed energy consumption analysis method is valid and efficient in improvements of energy efficiency in ethylene production systems. The DEA integrated ISM method is proposed.The proposed method can overcome the shortcomings of the DEA model.The energy efficiency framework of ethylene production process based on DEA integrated ISM is obtained.The proposed method is valid and efficient in improvement of energy efficiency in the ethylene plants.

Dimensionality Reduction with Input Training Neural Network and Its Application in Chemical Process Modelling

Abstract Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on improved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Momentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propylene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.

A data-attribute-space-oriented double parallel (DASODP) structure for enhancing extreme learning machine

Extreme learning machine (ELM), a simple single-hidden-layer feed-forward neural network with fast implementation, has been successfully applied in many fields. This paper proposes an ELM with a constructional structure (CS-ELM) for improving the performance of ELM in dealing with regression problems. In the CS-ELM, there are some partial input subnets (PISs). The first step in designing the PISs is to divide the data-attribute-space into several sub-spaces through using an improved extension clustering algorithm (IECA). The input data attributes in the same sub-space can build a PIS and the similar information of the data attributes is stored in the corresponding PIS. Additionally, a double parallel structure is applied in the CS-ELM, in which there is a special channel that directly connects the input layer neurons to the output layer neurons. In this regard, the proposed procedure can be called ELM with a data-attribute-space-oriented double parallel (DASODP) structure (DASODP-ELM). To test the validity of the proposed method, it is applied to 4 regression applications. The experimental results indicate that, compared with ELM, DASODP-ELM with less number of parameters can achieve higher regression precision in the generalization phase.

A New Extension Theory-based Production Operation Method in Industrial Process

Abstract To explore the problems of dynamic change in production demand and operating contradiction in production process, a new extension theory-based production operation method is proposed. The core is the demand requisition, contradiction resolution and operation classification. For the demand requisition, the deep and comprehensive demand elements are collected by the conjugating analysis. For the contradiction resolution, the conflict between the demand and operating elements are solved by the extension reasoning, extension transformation and consistency judgment. For the operating classification, the operating importance among the operating elements is calculated by the extension clustering so as to guide the production operation and ensure the production safety. Through the actual application in the cascade reaction process of high-density polyethylene (HDPE) of a chemical plant, cases study and comparison show that the proposed extension theory-based production operation method is significantly better than the traditional experience-based operation method in actual production process, which exploits a new way to the research on the production operating methods for industrial process.

Positive and negative correlation input attributes oriented subnets based double parallel extreme learning machine (PNIAOS-DPELM) and its application to monitoring chemical processes in steady state

Extreme learning machine (ELM) is an effective learning algorithm for single-hidden-layer feed-forward neural networks (SLFNNs). Due to its easiness in theory and implementation, ELM has been widely used in many fields. In order to further enhance the generalization performance of ELM, a positive and negative correlation input attributes oriented subnets based double parallel extreme learning machine (PCNCIAOS-DPELM) is proposed in this paper. A salient feature in the PNIAOS-DPELM is that there are two special subnets. In one of the two subnets, the input attributes have a positive correlation to the outputs. In another subnet, the input attributes have a negative correlation to the outputs. The two kinds of input attributes can be obtained by separating the input attributes into two categories using the correlation coefficient analysis. Then according to the categories, the two subnets can be established. The two subnets are based on well-trained auto-associative neural networks (AANNs), which can extract the nonlinear information of the input attributes and remove the redundant information. An advantage in PNIAOS-DPELM is that the proper number of the nodes in the hidden layer can be determined. To test the validity of PNIAOS-DPELM, it is applied to monitoring three chemical processes in steady state. Meanwhile, ELM, double parallel ELM (DP-ELM), and ELM with kernel (ELMK) were developed for comparisons. Experimental results demonstrated that PNIAOS-DPELM could achieve better regression precision and have better stable ability than ELM, DP-ELM, and ELMK did during the generalization phase.

A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement

In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications.

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

Abstract New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm (GA) based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and ex-periment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.

Research and Implementation of Decreasing the Acetic Acid Consumption in Purified Terephthalic Acid Solvent System

Decreasing the acetic acid consumption in purified terephthalic acid (PTA) solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network (GRNN) is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization (PSO) algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.

A fuzzy clustering–ranking algorithm and its application for alarm operating optimization in chemical processing

Alarm overload in modern chemical plants presents many difficulties in decision and diagnosis. Management and optimization of alarm information are challenging work that must be confronted everyday. A new system alarm optimization technique, based on a fuzzy clustering–ranking (FCR) algorithm, is proposed according to the correlativity among process‐measured variables. The fuzzy clustering method is used to rationally group and cluster the information matrix of alarm variables to effectively decrease alarms under safety production. Moreover, the fuzzy difference driving (FDD) algorithm is used to rank the clustering center and alarm variables in every cluster, based on objective process characteristics. Furthermore, the validity of the proposed algorithm and solution is verified by application of a practical ethylene cracking furnace alarm system. The proposed method is an effective and reliable alarm‐management method that can optimize process operation and improve plant safety in the chemical industry.