Yongming Han

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.

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

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A new Self-Organizing Extreme Learning Machine soft sensor model and its applications in complicated chemical processes

-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.

PID control loop performance assessment and diagnosis based on DEA-related MCDA

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.

Energy analysis and management method of complex chemical processes based on index decomposition analysis

Abstract The control of product quality of complex chemical processes strictly depends on the measure of the key process variables. However, the online measure device is extremely expensive, and these devices are hard to protect. Meanwhile, there is a delay for these online measure devices. Therefore, the soft sensor technology plays a vital role in measuring the key process variables. Extreme Learning Machine (ELM) is an efficient and simple single layer feed-forward neural networks (SLFNs) to building an exact soft sensor model. However, unsuitable selected hidden nodes and random parameters will greatly affect the performance of the ELM. Therefore, this paper proposes a novel Self-Organizing Extreme Learning Machine (SOELM) algorithm constructed by the biological neuron-glia interaction principle to solve the issue of the ELM. Firstly, the weights between input layer nodes and the CNS are tuned iteratively by the Hebbian learning rule. Then the network structure is adjusted self-organizing by Mutual Information (MI) among different structures of networks. Secondly, the weights between the CNS and output layer nodes are obtained by the ELM. The experimental results based on different UCI data sets prove that the SOELM has a better generalization capability and stability than that of the ELM. Moreover, our proposed method is developed as a soft sensor model for accurately predicting the key variables of the Purified Terephthalic Acid (PTA) process.

Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry

Control loop performance assessment and diagnosis have been attracting more and more attention in the academia and industry. Both traditional performance assessment method and minimum variance method often require the process model and provide limited information, which is not particularly convenient for practical applications. Therefore, the method based on data envelopment analysis (DEA)-related multiple criteria decision analysis (MCDA) is developed for assessing and diagnosing PID control loop performance, which relies solely upon the collected process data during routine plant operation. The control loop performance is assessed and sorted by utilizing the self-evaluation DEA-related MCDA model. The operation priority of the control loop is ranked and determined by utilizing the cross-evaluation DEA-related MCDA model. The improving direction and quantitative space of control loop performance can be diagnosed by DEA-related MCDA model with slack variables and non-Archimedean infinitesimal ε. The correctness and effectiveness of the proposed method are confirmed and validated by simulation examples.

Review: Multi-objective optimization methods and application in energy saving

Energy and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency, we put forward an energy analysis and management method based on index decomposition analysis (IDA). The proposed method can reflect the impact of energy usage by integrating the level of energy activity, energy hierarchy and energy intensity effectively. Meanwhile, energy efficiency improvement, energy consumption reduction and energy-savings can be visually disCovered by the proposed method. Finally, the proposed method is applied for energy management and conservation practices of the ethylene production process. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can propose corresponding improvement for the ethylene production.