Model is usually necessary for the design of a control loop.Due to simplification and unknown dynamics,model plant mismatch is inevitable in the control loop.In process monitoring,detection of mismatch and evaluation of its influences are demanded.In this paper several mismatch measures are presented based on different model descriptions.They are categorized into different groups from different perspectives and their potential in detection and diagnosis is evaluated.Two case studies on mixing process and distillation process demonstrate the efficacy of the framework of mismatch monitoring.

In recent years,immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications.However,IGA with deterministic mutation factor suffers from the problem of premature convergence.In this study,a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases,in which immune concepts are applied to determine the mutation parameters,is proposed to improve the searching ability of the algorithm and maintain population diversity.Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem.This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation,and satisfactory results are obtained.

This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints.The approach incorporates the virtues both from indirect and direct methods: it solves the optimality conditions like the traditional indirect methods do,but uses a discretization technique inspired from direct methods.Compared with other indirect approaches,the proposed approach has two main advantages: (1) the discretized optimization problem only employs unconstrained nonlinear programming (NLP) algorithms such as BFGS (Broyden-Fletcher-Goldfarb-Shanno),rather than constrained NLP algorithms,therefore the computational efficiency is increased; (2) the relationship between the number of the discretized time intervals and the integration error of the four-step Adams predictor-corrector algorithm is established,thus the minimal number of time intervals that under desired integration tolerance can be estimated.The classic batch reactor problem is tested and compared in detail with literature reports,and the results reveal the effectiveness of the proposed approach.Dealing with path constraints requires extra techniques,and will be studied in the second paper.

In this paper,a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants.Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese,a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints.Subsequently,chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way.Accordingly,theoretical definitions,adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms.Finally,an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion.Firstly,the solution in the algorithm is represented as job permutation.Secondly,an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity.Thirdly,based on the idea of iterated greedy algorithm,some newly designed schemes for employed bee,onlooker bee and scout bee are presented.The performance of the proposed algorithm is tested on the well-known Taillard benchmark set,and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm.In addition,the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.

In this paper,an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields.In order to improve the global searching ability and convergence speed,IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm.Moreover,the feasibility rules are used to handle constraints,which do not require additional parameters and can guide the population to the feasible region quickly.The effectiveness of hybridization mechanism of IHDE-EDA is first discussed,and then simulation and comparison based on three benchmark problems demonstrate the efficiency,accuracy and robustness of IHDE-EDA.Finally,optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems.The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation.Bottleneck is the key factor to a SM system,which seriously influences the throughput rate,cycle time,time-delivery rate,etc.Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling.Because categorical data (product types,releasing strategies) and numerical data (work in process,processing time,utilization rate,buffer length,etc.) have significant effect on bottleneck,an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs.In this improved ANFIS,the contribution of categorical inputs to firing strength is reflected through a transformation matrix.In order to tackle high-dimensional inputs,reduce the number of fuzzy rules and obtain high prediction accuracy,a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system.According to the experimental results,the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.

Cracking gas compressor is usually a centrifugal compressor.The information on the performance of a centrifugal compressor under all conditions is not available,which restricts the operation optimization for compressor.To solve this problem,two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer.The input data of the model under other conditions should be corrected according to the similarity theory.The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor.The result shows that it is an effective method to optimize the compressor system.

An expansion procedure to design partially decentralized controllers via model predictive control is proposed in this paper.Partially decentralized control is a control structure that lies between a fully decentralized structure and a fully centralized one,and has the advantage of achieving comparable performance as a fully centralized controller but with simpler structure.The proposed method follows the expansion method proposed in a previous paper where internal model control (IMC) was used to design controllers for non-square subsystems.The method requires computing the pseudo-inverse of a non-square matrix via pseudo-inverse factors.Instead,the proposed method uses dynamic matrix control (DMC) to design PID controllers for non-square subsystems without using additional factors.The effectiveness of the proposed method is demonstrated on several chemical examples.Simulation results show that the proposed method is simple and can achieve better performance.

For dealing with large static error due to poor immunity of the traditional fuzzy control,a novel interval type-2 fuzzy control system is proposed.By extending the typical membership functions to interval type-2 membership functions,the proposed control system can efficiently reduce the uncertain disturbance from real environment without increasing the design complexity.The simulation results on the water tank level control system showed that the proposed method succeeded in better static and dynamic control with stronger robust performance than the traditional fuzzy control method.

Since the combustion system of coal-fired boiler in thermal power plant is characterized as time varying,strongly coupled,and nonlinear,it is hard to achieve a satisfactory performance by the conventional proportional integral derivative (PID) control scheme.For the characteristics of the main steam pressure in coal-fired power plant boiler,the sliding mode control system with Smith predictive structure is proposed to look for performance and robustness improvement.First,internal model control (IMC) and Smith predictor (SP) is used to deal with the time delay,and sliding mode controller (SMCr) is designed to overcome the model mismatch.Simulation results show the effectiveness of the proposed controller compared with conventional ones.

The distillation column with side reactors (SRC) can overcome the temperature/pressure mismatch in the traditional reactive distillation,the column operates at temperature/pressure favorable for vapor-liquid separation,while the reactors operate at temperatures/pressures favorable for reaction kinetics.According to the smooth operation and automatic control problem of the distillation column with side reactors (SRC),the design,simulation calculation and dynamic control of the SCR process for chlorobenzene production are discussed in the paper.Firstly,the mechanism models,the integrated structure optimal design and process simulation systems are established,respectively.And then multivariable control schemes are designed,the controllability of SRC process based on the optimal steady-state integrated structure is explored.The dynamic response performances of closed-loop system against several disturbances are discussed to verify the effectiveness of control schemes for the SRC process.The simulating results show that the control structure using conventional control strategies can effectively overcome feeding disturbances in a specific range.

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes.Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis.In this paper,abundant phase information is revealed by way of partitioning MPCA model,and a new phase identification method based on global dynamic information is proposed.The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding,phase division and process monitoring.

Closed-loop identification is important and necessary to various model-based advanced process control strategies,whose performance depends greatly on the informative property of the data set.Switching control is an important method in process control.Therefore,this paper studies the informative property of a data set in a single-input single-output (SISO) closed-loop system with a switching controller.It is proved that this data set is informative if the controller switches among at least two modes (i.e.,feedback laws).Our result does not require any assumption on the way of switch and removes the constraints on the switching manner required in some classical literature.Finally,simulation case studies based on a continuous stirred-tank reactor (CSTR) process are given to validate the results.

A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations,known as the error-in-variables (EIV) problem.Using the orthogonal projection approach to eliminate the noise influence,consistent estimation is guaranteed for the deterministic part of such a system.A strict proof is given for analyzing the rank condition for such orthogonal projection,in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model.In the result,the plant state matrices can be retrieved in a transparent manner from the above matrices.An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.

Chemical processes are complex,for which traditional neural network models usually can not lead to satisfactory accuracy.Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks,but there are some problems,e.g.,lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small.In this study,the output errors of networks are vectorized,the diversity of networks is defined based on the error vectors,and the size of ensemble is analyzed.Then an error vectorization based selective neural network ensemble (EVSNE) is proposed,in which the error vector of each network can offset that of the other networks by training the component networks orderly.Thus the component networks have large diversity.Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

Based on the immune mechanics and multi-agent technology,a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced.Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem,and combines the global and local search in the searching process.The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms.The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions.Furthermore,Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.

This paper mainly aims at the modeling problem of the photovoltaic (PV) array with a 30 kW PV grid-connected generation system.An iterative method for the time-varying parameters is proposed to model a plant of PV array.The relationship of PV cell and PV array is obtained and the solution for PV array model is unique.The PV grid-connected generation system is used to demonstrate the effectiveness of the proposed method by comparing the calculated values with the actual output of the system.

More and more information are needed in social life and commercial production,causing significant pressure on the sampling and too much time spent on signal sampling.Compressed sensing is one emerging hotspot in signal processing which employs a special sampling method to capture and represent compressible signals at a rate significantly below the Nyquist rate.In this paper,a Takagi-Sugeno-Kang (TSK) Model based on compressed-sensing sampling theorem is proposed for grinding power.It is further tested by using the actual production data,and the algorithm performance in grinding power model is also analyzed.The experiments show the validity and effectiveness of the proposed modeling method and its bright application foreground in other fields with similar features,such as power,metallurgy and so on.

In the literature,many works on determining inspection interval by the delay time concept generally assumed that the time of inspection span and the time of failure renewal are negligible.However,in realistic cases,the above time should not be neglected for both the time itself and its effect.In order to model the effect of the possible overlapping of inspection span with failure downtime on determining the optimal inspection interval,we propose a block-based inspection model for a single component-based on delay time concept.We further compare this model with the age-based model to show the practical sense.The developed models are also demonstrated by numerical examples.

For complex industrial processes with multiple operational conditions,it is important to develop effective monitoring algorithms to ensure the safety of production processes.This paper proposes a novel monitoring strategy based on fuzzy C-means.The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection.Then the scores in the novel subspace are classified into several overlapped clusters,each representing an operational mode.The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index.The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production.The ammonia concentration at the ammonia converter outlet is a significant process variable,which reflects directly the production efficiency.However,it is hard to be measured reliably online in real applications.In this paper,a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration.A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN.GSO-NH is integrated with BPNN to build a soft sensor model.Finally,the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application.Three other modeling methods are applied for comparison with GSO-NH-NN.The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy.Moreover,the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information.However,few of them consider phase transitions,though they exit widely in batch processes and have non-ignorable impacts on product qualities.In the present work,a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions.First,batch processes are divided into several phases using regression parameters other than prior process knowledge.Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods.Finally,based on the analysis of different characteristics of transitions and steady phases,an integrated algorithm is developed for quality prediction.The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.

Laboratory tests were carried out to study the breakage kinetics of diasporic bauxite and determine its breakage distribution function.Non-first order breakage with different deceleration rates for different size intervals is found,which is most probably caused by the heterogeneity of the ore.Piecewise linearization method is proposed to describe the non-first order breakage according to its characteristics.In the method,grinding time is divided into several intervals and breakage is assumed to be first order in each interval.So,the breakage rates are calculated by taking the product of the last interval as feed and then established as a function of particle size and grinding time.Based on the predetermined breakage rate function,the breakage distribution of the ore is back-calculated from the experimental data using the population balance model (PBM).Finally,the obtained breakage parameters are validated and the simulated data are in good agreement with the experimental data.The obtained breakage distribution and the method for breakage rate description are both significant for modeling the full scale ball milling process of bauxite.

Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization.Since the MPC does not handle disturbances directly by controller design,it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties.In this paper,an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation.The improved DOB is based on the optimal achievable H₂ performance and can deal with disturbance observation for the nonminimum-phase delay systems.In this DOB-MPC advanced feedback control,the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level.The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system.The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties.Several simulations are performed to demonstrate the proposed control method for grinding process operation.

Soft-sensing is widely used in industrial applications.The traditional soft-sensing structure is open-loop without correction mechanism.If the working condition is changed or there is unknown disturbance,the forecast result of soft-sensing model may be incorrect.In order to obtain accurate values,it is necessary to carry out online correction.In this paper,a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach,which estimates the input variables in the next moment by a prediction model and calibrates the output variables by a compensation model.The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models.

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs),three learning rate adaptation methods (heuristic rule,delta-delta rule,and delta-bar-delta rule),which are used to speed up training in artificial neural networks,are used to develop the training algorithms for feedforward SNN.The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem,Iris dataset,fault diagnosis in the Tennessee Eastman process,and Poisson trains of discrete spikes.The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm.Furthermore,if the adaptive learning rate is used in combination with the momentum term,the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence.In the three learning rate adaptation methods,delta-bar-delta rule performs the best.The delta-bar-delta method with momentum has the fastest convergence rate,the greatest stability of training process,and the maximum accuracy of network learning.The proposed algorithms in this paper are simple and efficient,and consequently valuable for practical applications of SNN.