Search

The Kumar model as a molecular model has achieved successful application. However, only 22 reactions limit its veracity and adaptability for feedstocks. A series of models with different degrees of integration of the free radical model and the molecular model has been proposed to enhance feedstock adaptability and simulation accuracy. An improved search engine algorithm, namely Improved PageRank (IPR), is provided and applied to calculate the importance of substances in Kumar model to screen the free-radical reaction network for efficient model selection. A methodology of optimal structure and model parameters chosen is applied to the target to improve the adaptability of the material and the accuracy of the model. Then, two cases with different feedstocks are demonstrated with industrial data to verify the correctness of the proposed approach and its wide feedstock adaptability. The proposed model demonstrates good performance: (1) The mean relative errors (MRE) of the K-R (Kumar and free-radical) model have reached an order of magnitude less than 0.1% compared with 5% in the Kumar model. Further, (2) the K-R model can be implemented to model some feedstocks which Kumar model can&rsquo, t simulate successfully. The K-R model can be applied in simulation of extensive feedstocks with high accuracy.

Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.

As the most effective indicator for energy saving and emission reduction, energy efficiency evaluation is widely used in complex petrochemical industries. It is nowadays common to combine traditional mechanism methods based on momentum transport, energy transport, quality transport (TT) and reaction engineering (RG) (TT-RG), with data-driven artificial intelligence methods. Using the combined method to achieve production optimization and energy saving by analyzing the evaluation indicator of energy efficiency has gradually become an important part in complex petrochemical industries. Therefore, this paper introduced the main methods and the latest research results of energy efficiency evaluation of complex petrochemical industries. These methods are mainly divided into three parts, including the mechanism methods based on TT-RG, the data-driven artificial intelligence methods, and the hybrid methods combining the mechanism and the data-driven. Then, different methods are compared and described in detail. Moreover, the best method for evaluating the energy efficiency can be found to provide theoretical guidance for energy saving and emission reduction of complex petrochemical industries. Finally, the future development direction for energy efficiency evaluation in complex petrochemical industries is given.

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed: (1)the adjacency matrix is determined by partial correlation coefficient; (2)the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram; (3)interpretative structural for large-scale process control system is built by this ISM method; and (4)non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

The alarm system given in industrial plants are massive and complex. Under such condition, critical alarms are overwhelmed by false and unnecessary alarms and thus result in severe safety issues. To address the problem, this paper proposes a probabilistic signed digraph (PSDG) based alarm signal selection method that requires achieving maximal system reliability. In this method, a PSDG model is firstly constructed to visualize the causal relations between process variables. Then the criteria of observability and identifiability are imposed to determine the candidate alarm variables that can qualitatively distinguish all assumed faults. Instead of selecting the minimum number of combinations of candidate variables, the alarm variables are optimized by a reliability formulation that takes into account the missed alarm and false alarm probabilities of the system; this formulation is solved by the receiver operating characteristic (ROC) graph. Finally, the developed methodology is illustrated using a Tennessee Eastman process.

Data envelopment analysis (DEA) has been widely used for efficiency evaluation of industrial plants. A conventional DEA model may easily lead to the situation where more than one-third of efficiency values are set to 1, so it is hard to analyze the pros and cons of the multi-decision-making units. The DEA cross-model can distinguish the pros and cons of the effective decision-making units, but it is unable to indicate the improvement direction of the ineffective decision-making units. This paper proposes an efficiency analysis method based on an improved DEA cross-model, which can get higher efficiency discrimination in identifying the efficiency state of the decision-making units compared with the conventional DEA model. The improved DEA cross-model avoids the impacts of unreasonable weight allocation of input and output indices. Meanwhile, self-evaluation of the improved DEA cross-model with the slack variables can find the improvement direction of the ineffective decision-making units. Also, the Malmqu...

Various grades of polyethylene are required to be produced in the same industrial equipment by changing the operating conditions. However, transitions between different grades are rather slow and r...

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.

A precise mechanistic model is fundamental in simulation, control and optimization of ethylene pyrolysis process. In the Kumar kinetics model proposed by Kumar and Kunzru, there are ten changeable selectivity coefficients and minor-changeable kinetics parameters in the primary reaction equation. They are mainly determined by oil properties, especially the oil composition. This paper proposes a new method of compromising adjustment solution based on target decomposition strategy and Error Correction Chaos Optimization (ECCO) algorithm to mainly acquire rational primary coefficients, and kinetics parameters when necessary. The core idea is to realize atoms balances of the carbon and hydrogen in the primary reaction, as well as minimum yield errors of products based on ECCO algorithm. It exceeds existing methods mentioned by other literatures in its preferable compromise between overall elemental balances and minimal yield errors. It also takes minor tuning of kinetics parameters into consideration. Moreover, this method has been proven effective and applicable to many naphthas with different properties. These data are from a real ethylene plant. The results are helpful in the design of optimal operational conditions and ultimately in controlling of ethylene cracking furnaces which will be discussed briefly.

The Kumar molecular reaction kinetics model (proposed by Kumar and Kunzru) is widely used to establish an ethylene cracking model because of its accuracy and simplicity. Parameters of the primary reaction equation are determined definitely by different oil properties and compositions. These parameters include changeable selectivity coefficients and minor-changeable kinetics parameters, which should be optimized in terms of different oils in an actual process. The paper proposes a new optimization algorithm based on a hybrid sequential quadratic programming–chaos particle swarm optimization (SQP-CPSO) to realize two optimal targets: elemental balances of carbon and hydrogen atoms, and minimum product yield errors. The SQP algorithm is to accelerate the local search in the CPSO global optimum process. The SQP-CPSO algorithm first acquires the optimized selectivity coefficients of non-linear kinetics modelling, and then if the elemental balance is not reached, the kinetics parameters are optimized to satisfy both optimal targets. The experimental results exceed the existing methods in its preferable realization of two targets. Moreover, this method has been proven effective and applicable with several kinds of naphtha with different characteristics from a real ethylene plant. The optimized kinetics model is helpful in predicting product yields, and optimizing and controlling operational conditions in an ethylene product system.

Data fusion is an emerging technology to fuse data from multiple data or information of the environment through measurement and detection to make a more accurate and reliable estimation or decision. In this Article, energy consumption data are collected from ethylene plants with the high temperature steam cracking process technology. An integrated framework of the energy efficiency estimation is proposed on the basis of data fusion strategy. A Hierarchical Variable Variance Fusion (HVVF) algorithm and a Fuzzy Analytic Hierarchy Process (FAHP) method are proposed to estimate energy efficiencies of ethylene equipments. For different equipment scales with the same process technology, the HVVF algorithm is used to estimate energy efficiency ranks among different equipments. For different technologies based on HVVF results, the FAHP method based on the approximate fuzzy eigenvector is used to get energy efficiency indices (EEI) of total ethylene industries. The comparisons are used to assess energy utilization...

For fuzzy multi-attribute decision-making, a fuzzy symmetry matrix, by referring to covariance definition of random variables, is constructed as attribute evaluation space based on fuzzy decision-making matrix. Improving the fuzzy Analytic Hierarchy Process (AHP) method is proposed by using the approximate fuzzy eigenvector of such fuzzy symmetry matrix. This algorithm reflects the dispersed projection of decision information in general. It has better objectivity and resolving power for the decision-making. This algorithm is used for illustration and comparison with other methods. The results are applied in an example to illustrate that this algorithm is more efficient and objective for multi-attribute decision-making application.

A new method of wavelet theory-based self-adaptive multi-scale principal component analysis (W-AMSPCA) is proposed for process signal monitoring and diagnosis. The technique uses the wavelet analysis to decompose the signals and reconstruct the signals in order to denoise with disturbance and outliers, and then uses the adaptive PCA algorithm 10 reduce the dimensions of process signals and identify different wavelet coefficients based on the multi-scale decomposition. The proposed method can early find and analyze the slow and feeble changes of process signals that can't be monitored by normal PCA. Furthermore, the performing framework and algorithm of W-AMSPCA for on-line signal monitoring and diagnosis are proposed.