Zhiqiang Geng

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.

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.

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 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.

Energy Efficiency Prediction Based on PCA-FRBF Model: A Case Study of Ethylene Industries

Energy conservation and emission reduction in the ethylene industry is the main way to attain sustainable development, which can be achieved if the energy efficiency of petrochemical industries can be accurately analyzed and predicted. This paper proposes an improved radial basis function neural network based on fuzzy

A MULTISWARM COMPETITIVE PARTICLE SWARM ALGORITHM FOR OPTIMIZATION CONTROL OF AN ETHYLENE CRACKING FURNACE

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A New Fuzzy Process Capability Estimation Method Based on Kernel Function and FAHP

-means (FCM) algorithm integrated with principal component analysis (PCA) technology (PCA-FRBF). The PCA is used to denoise and reduce dimensions of data to decrease the training time and errors of the modeling process. The FCM is used to separate every fuzzy class in input space and decide the number of neurons in hidden layer to overcome the shortcoming of setting them by experience subjectively. Meanwhile, the robustness and effectiveness of the PCA-FRBF model are validated through the standard data set from the University of California Irvine repository. Moreover, to predict the energy efficiency of ethylene plants, a multi-inputs and single-output model of energy efficiency is established based on the PCA-FRBF for monthly data of ethylene production process. We obtain a rational allocation of crude oil, fuel, steam, water, and electricity, and the greatest benefit of ethylene plants under different technologies. Finally, the empirical results show the effectiveness and practicability of the PCA-FRBF model applied to predict and guide the ethylene production in the petrochemical industry.

Process monitoring based on improved recursive PCA methods by adaptive extracting principal components

A fuzzy C-means (FCM) multiswarm competitive particle swarm optimization (FCMCPSO) algorithm is proposed, in which FCM clustering is used to divide swarms adaptively into different clusters. The large-scale swarms are according to the standard particle swarm optimization (PSO) algorithm, whereas the small-scale swarms search randomly in the neighborhood of the optimal solution to increase the probability of jumping out of the local optimization point. Within every cluster, the adaptive value gained by competitive learning is respectively found and arranged in order. Swarms of small adaptive value were integrated with the neighboring swarms of large adaptive value to search the optimal solution competitively by the swarms. The algorithms validity was tested by benchmark functions and compared with other PSO algorithms. Furthermore, an integrated FCMCPSO-radial basis function neural network was studied for nonlinear system modeling and intelligent optimization control of cracking depth of an ethylene cracking furnace application in a chemical process.

Hierarchy probability cost analysis model incorporate MAIMS principle for EPC project cost estimation

Because of more and more complexity of an operation environment in todays industrial production process, it is difficult to monitor the process operation quality and to estimate the performance efficiency based on the existing mathematical model and knowledge. This paper proposes a new method to estimate the process capability, and a new criterion for capability and performance assessment. This method is based on kernel function and fuzzy analysis hierarchy process (FAHP), which can improve the adaptation of process capability analysis. The device process capability can be estimated by FAHP with main variables, which are determined by interpretive structure modeling. The estimators of these indices overcome uncertainties caused by data fluctuation in the traditional process capability, and could strongly improve the robustness and adaptability of the process capability estimation and diagnosis. The proposed methods are used in a simulation of the Tennessee Eastman process. The results demonstrate the efficiency and validity of the presented approach. The proposed method can provide more performance decision information of industrial process to help decision makers evaluate and diagnose the state of the production devices, and improve the process operations.