H_∞ Control of Bio-dissimilation Process of Glycerol to 1,3-Propanediol

A robust controller is designed by using the bilinear transformation and H∞ mixed sensitivity method for bio-dissimilation process of glycerol to 1,3-propanediol. Under the controller the system works near an optimal steady-state for the volumetric productivity of 1,3-propanediol attaining its maximization. The design procedure is carried out by tuning the transformation parameter and DC gain of the performance weighted function, which is an iterative and optimal search process. Simulation results are presented which show that the designed robust controller not only ensures the robust stability of the system in face of the parametric variations in the model, but also makes the system have a favourable robust tracking performance. The validity of the proposed H∞ controller has been tested.

Sensorless torque control scheme of induction motor for hybrid electric vehicle

In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle （HEV） applications is addressed, Because motor parameter variations in HEV applications are larger than in industrial drive system, the conventional field-oriented control （FOC） provides poor performance. Therefore, a new robust PI-based extension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed in order to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme is robust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor is chosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy losses shown by simulations.

Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process

It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.

Observer-based adaptive control of uncertain time-delay switched systems with stuck actuator faults

This paper concerns the observer-based adaptive control problem of uncertain time-delay switched systems with stuck actuator faults. Under the case where the original controller cannot stabilize the faulty system, multiple adaptive controllers are designed and a suitable switching logic is incorporated to ensure the closed-loop system stability and state tracking. New delay-independent sufficient conditions for asymptotic stability are obtained in terms of linear matrix inequalities based on piecewise Lyapunov stability theory. On the other hand, adaptive laws for on-line updating of some of the controller parameters are also designed to compensate the effect of stuck failures. Finally, simulation results for reference [1] model show that the design is feasible and efficient.

An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data

Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and system rules are defined as specific structures.Second,to speed up the convergence of the learning algorithm and lighten the oscillation,an improved descent method for FIS generation is developed.Furthermore, the convergence and the oscillation of the algorithm are system- atically analyzed.Third,using the information obtained from the previous phase,it can be decided in which region of the in- put space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased.Consequently,this produces a new and more appropriate structure.Finally,the proposed method is applied to the problem of nonlinear function approximation.

Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases

Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control （APC）. For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function （RBF） neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.

Soft Sensing Modelling Based on Optimal Selection of Secondary Variables and Its Application

The composition of the distillation column is a very important quality value in refineries, unfortunately, few hardware sensors are available on-line to measure the distillation compositions. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression （KRR） to implement on-line soft sensing of distillation compositions is proposed. In this approach, the sensitivity matrix analysis is presented to select the most suitable secondary variables to be used as the soft sensor＇s input. The KRR is used to build the composition soft sensor. Application to a simulated distillation column demonstrates the effectiveness of the method.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Two-tier control structure design methodology applied to heat exchanger networks

Because of its paramount importance in the successful industrial control strategy of a given heat exchanger network(HEN),the control structure designs for providing appropriate manipulated variable(MV)and controlled variable pairings have received considerable attention.However,quite frequently HENs with such control structures face the problem of hard constraints,typically holding the HENs at less controlled operating space.So both the MV pairings and the above control pairings should be considered to design a control structure.This paper investigates the systematic incorporation of the two pairings,and presents a methodology for designing such two-tier control structure.This is developed based on the sequential strategy,coupling an indirect-tier with direct-tier control structure design,wherein the intention is realized in the former stage and the latter is implemented for further optimization.The MV identification and pairing are achieved through variations in heat load of heat exchangers to design the indirect-tier control structure.Then the direct-tier control structure is followed the relative gain array pairing rules.With the proposed methodology,on the one hand,it generates an explicit connection between the MV pairings and the HEN configuration,and the quantitative interaction measure is improved to avoid the multiple solutions to break the relationship among all the control pairings into individuals;on the other hand,a two-tier control structure reveals control potentials and control system design requirements,this may avoid complex and economically unfavourable control and HEN structures.The application of proposed framework is illustrated with two cases involving the dynamic simulation analysis,the quantitative assessment and the random test.

Energy efficiency evaluation based on DEA integrated factor analysis in ethylene production

Energy efficiency evaluation plays an important role in energy efficiency improvement of the ethylene production. It is observed from the actual production data that the ethylene production energy efficiency often varies with the complex production working conditions. In the favored methods for energy efficiency evaluation,DEA models may show poor resolution when directly used to evaluate the efficiency values. Therefore, a new energy efficiency evaluation method for ethylene production is proposed based on DEA integrated factor analysis with respect to operation classification. Three key factors, including raw material composition, cracking depth and load rate, are taken into account in determining the production working conditions by means of k-means algorithm. Based on the multi-working conditions mode the energy efficiency evaluation of the ethylene production is made by using DEA model, where the most related energy data are screened by factor analysis.Furthermore, the supporting decision of energy efficiency improvement is provided to the operators. The accuracy and effectiveness of the proposed method are illustrated by applying in a practical ethylene production,which gives more effective energy efficiency evaluation in the complicated working conditions of ethylene production with declined dimension of input indicators.

针对有测量偏差(EIV)的一种增广闭环子空间辨识方法(英文)

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.

有色噪声干扰下时滞过程输出误差模型的迭代辨识（英文）

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation

Ethylene cracking process is the core production process in ethylene industry,and is paid more attention to reduce high energy consumption.Because of the interdependent relationships between multi-flow allocation and multi-parameter setting in cracking process,it is difficult to find the overall energy efficiency scheduling for the purpose of saving energy.The traditional scheduling solutions with optimal economic benefit are not applicable for energy efficiency scheduling issue due to the neglecting of recycle and lost energy,as well as critical operation parameters as coil outlet pressure(COP)and dilution ratio.In addition,the scheduling solutions mostly regard each cracking furnace as an elementary unit,regardless of the coordinated operation of internal dual radiation chambers(DRC).Therefore,to improve energy utilization and production operation,a novel energy efficiency scheduling solution for ethylene cracking process is proposed in this paper.Specifically,steam heat recycle and exhaust heat loss are considered in cracking process based on 6 types of extreme learning machine(ELM)based cracking models incorporating DRC operation and three operation parameters as coil outlet temperature(COT),COP,and dilution ratio according to semi-mechanism analysis.Then to provide long-term decision-making basis for energy efficiency scheduling,overall energy efficiency indexes,including overall output per unit net energy input(OONE),output-input ratio per unit net energy input(ORNE),exhaust gas heat loss ratio(EGHL),are designed based on input-output analysis in terms of material and energy flows.Finally,a multiobjective evolutionary algorithm based on decomposition(MOEA/D)is employed to solve the formulated multi-objective mixed-integer nonlinear programming(MOMINLP)model.The validities of the proposed scheduling solution are illustrated through a case study.The scheduling results demonstrate that an optimal balance between multi-flow allocation,multi-parameter setting,and DRC coordinated operation is reached,which achieves 3.37%and 2.63%decreases in net energy input for same product output and conversion ratio,as well as the 1.56%decrease in energy loss ratio.

Robust stability analysis of switched uncertain systems with multiple time-varying delays and actuator failures

In this paper,the robust stability issue of switched uncertain multidelay systems resulting from actuator failures is considered.Based on the average dwell time approach,a set of suitable switching signals is designed by using the total activation time ratio between the stable subsystem and the unstable one.It is first proven that the resulting closed-loop system is robustly exponentially stable for some allowable upper bound of delays if the nominal system with zero delay is exponentially stable under these switching laws.Particularly,the maximal upper bound of delays can be obtained from the linear matrix inequalities.At last,the effectiveness of the proposed method is demonstrated by a simulation example.

On Some Aspects of Perturbation Analysis for Matrix Cone Optimization Induced by Spectral Norm

In this paper,we consider a cone problem of matrix optimization induced by spectral norm(MOSN).By Schur complement,MOSN can be reformulated as a nonlinear semidefinite programming(NLSDP)problem.Then we discuss the constraint nondegeneracy conditions and strong second-order sufficient conditions of MOSN and its SDP reformulation,and obtain that the constraint nondegeneracy condition of MOSN is not always equivalent to that of NLSDP.However,the strong second-order sufficient conditions of these two problems are equivalent without any assumption.Finally,a sufficient condition is given to ensure the nonsingularity of the Clarke’s generalized Jacobian of the KKT system for MOSN.