EXPERT SYSTEM FOR CONTROLLING THE STATE OF SINTERING PROCESS

In order to control sintering process,improve permeability and stabilize burn through point, a control scheme which combines thermal state with permeability state is proposed, and an expert system for controlling sintering process state is developed, the software which includes about 1000 expert rules is successfully applied to off line control of sintering process.

Intelligent optimal control for lead-zinc sintering process state

The intelligent integrated predictive model of synthetical permeability was established using the fuzzy classifier to combine the time sequence predictive model with the craftwork parameter predictive model. Then, the estimation model of burn-through point(BTP) based on pipe stress point(PSP) method and the predictive model of BTP were proposed. The optimal control of permeability and heat states was implemented by using the fuzzy expert controller with self-studying mechanism. The application of the intelligent control technique suppresses 17% of the fluctuation of synthetical permeability and 12% of the fluctuation of BTP, stabilizes the output and quality of sinter and settles the basis for the optimization of output and quality of sintering process.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.

State Space Model Predictive Control of an Aerothermic Process with Actuators Constraints

This paper investigates State Space Model Predictive Control (SSMPC) of an aerothermic process. It is a pilot scale heating and ventilation system equipped with a heater grid and a centrifugal blower, fully connected through a data acquisition system for real time control. The interaction between the process variables is shown to be challenging for single variable controllers, therefore multi-variable control is worth considering. A multi-variable state space model is obtained from on-line experimental data. The controller design is translated into a Quadratic Programming (QP) problem, in which a cost function subject to actuators linear inequality constraints is minimized. The outcome of the experimental results is that the main control objectives, such as set-point tracking and perturbations rejection under actuators constraints, are well achieved for both controlled variables simultaneously.

Optimizing Control of Bio-dissimilation Process of Glycerol to 1,3-Propanedlol

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.

A modified active disturbance rejection control for a wastewater treatment process

Waste water treatment process(WWTP)control has been attracting more and more attention.However,various undesired factors,such as disturbance,uncertainties,and strong nonlinear couplings,propose big challenges to the control of a WWTP.In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality,rather than take the model dependent control approaches,an active disturbance rejection control(ADRC)is utilized.Based on the control signal and system output,a phase optimized ADRC(POADRC)is designed to control the dissolved oxygen and nitrate concentration in a WWTP.The phase advantage of the phase optimized extended state observer(POESO),convergence of the POESO,and stability of the closed-loop system are analyzed from the theoretical point of view.Finally,a commonly accepted benchmark simulation model no.1.(BSM1)is utilized to test the POESO and POADRC.Linear active disturbance rejection control(LADRC)and the suggested proportion-integration(PI)control are taken to make a comparative research.Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control.Numerical results show that,as a result of the leading phase of the total disturbance estimation,the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.

Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,

Formal Reduction of Interfaces to Large-scale Process Control Systems

A formal methodology is proposed to reduce the amount of information displayed to remote human operators at interfaces to large-scale process control plants of a certain type. The reduction proceeds in two stages. In the first stage, minimal reduced subsets of components, which give full information about the state of the whole system, are generated by determining functional dependencies between components. This is achieved by using a temporal logic proof obligation to check whether the state of all components can be inferred from the state of components in a subset in specified situations that the human operator needs to detect, with respect to a finite state machine model of the system and other human operator behavior. Generation of reduced subsets is automated with the help of a temporal logic model checker. The second stage determines the interconnections between components to be displayed in the reduced system so that the natural overall graphical structure of the system is maintained. A formal definition of an aesthetic for the required subgraph of a graph representation of the full system, containing the reduced subset of components, is given for this purpose. The methodology is demonstrated by a case study.

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.

从日本PSC检查分析船舶安全管理之要点

通过分析日本实行PSC的情况, 研究我国船舶如何加强安全管理, 降低在日本的滞留率, 争取无缺陷通过PSC.

PSC:软硬件协同设计的一种中间模型

提出了一种软硬件协同设计的系统模型—— PSC(进程状态控制 )。建立了 PSC的可视化表示、形式化的定义和演化规则。PSC是一种层次化建模方法 ,它可以将系统控制逻辑和功能实现分离 ,有助于将模型转换为物理实现 。

A new classification of seepage control mechanisms in geotechnical engineering

Seepage flow through soils,rocks and geotechnical structures has a great influence on their stabilities and performances,and seepage control is a critical technological issue in engineering practices.The physical mechanisms associated with various engineering measures for seepage control are investigated from a new perspective within the framework of continuum mechanics;and an equation-based classification of seepage control mechanisms is proposed according to their roles in the mathematical models for seepage flow,including control mechanisms by coupled processes,initial states,boundary conditions and hydraulic properties.The effects of each mechanism on seepage control are illustrated with examples in hydroelectric engineering and radioactive waste disposal,and hence the reasonability of classification is demonstrated.Advice on performance assessment and optimization design of the seepage control systems in geotechnical engineering is provided,and the suggested procedure would serve as a useful guidance for cost-effective control of seepage flow in various engineering practices.

Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation tasks.However,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic systems.In this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb exoskeleton.The dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain subsystems.To facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control signal.The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics.Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

熟悉PSC检查中GMDSS缺陷降低船舶滞留风险

介绍GMDSS滞留缺陷的判定,列举了PSC检查中常见的滞留缺陷,并提出相关建议以期减少船舶的滞留风险。

STEADY-STATE OPERATION ANALYSIS OF AN IDEAL HEAT INTEGRATED DISTILLATION COLUMN

A systematic approach for the steady-state operation analysis of chemical processes is pro-posed.The method affords the possibility of taking operation resilience into consideration during thestage of process design.It may serve the designer as an efficient means for the initial screening ofalternative design schemes.An ideal heat integrated distillation column(HIDiC),without any reboileror condenser attached,is studied throughout this work.It has been found that among the various va-riables concerned with the ideal HIDiC,feed thermal condition appears to be the only factor exertingsignificant influences on the interaction between the top and the bottom control loops.Maximuminteraction is expected when the feed thermal condition approaches 0.5.Total number of stages andheat transfer rate are essential to the system ability of disturbance rejection.Therefore,more stagesand higher heat transfer rate ought to be preferred.But,too many stages and higher heat transfer ratemay increase the load of the

E-PSC研究与实践

文章对E-PSC(电子化港口国监督检查)进行研究,以理论与实际相结合的方式,对E-PSC流程进行介绍分析,并对进一步发展提出建议。

A Rapid and Simple Quantitative Method for the Active Ingredients of Aescin in the Extraction Process Using Near Infrared Spectroscopy

To achieve a rapid and simple detection for the active ingredients of Aescin in the extraction process using near-infrared spectroscopy (NIR) and to realize the state monitoring and quality control of the extraction process. Partial least square regression (PLS) was applied to build the near-infrared calibration models, and the applicability of the model was investigated by predicting the unknown samples in the extraction process. The correlation coefficients of the established Aescin models (A, B, C, D) were 0.9836, 0.9831, 0.9833, 0.9824, and the prediction standard deviations (SEP) were 0.05636, 0.05043, 0.02412, 0.05636, respectively. This study suggests that the proposed model has superior stability and accuracy. NIR spectroscopy technique provides a novel efficient and environmentally friendly approach to the rapid determination of four Aescin key quality indicators (A, B, C, D) in the extraction, which was solved the problem that the lack of state monitoring during the extraction of Aescin, thereby improved the quality of Aescin.

基于二阶价值梯度模型强化学习的工业过程控制方法

为了实现对高延时、非线性和强耦合的复杂工业过程稳定准确的连续控制,提出了一种基于二阶价值梯度模型强化学习的控制方法。首先,该方法在模型训练过程中加入了状态价值函数的二阶梯度信息,具备更精确的函数逼近能力和更高的鲁棒性,学习迭代效率更高;其次,通过采用新的状态采样策略,可以更高效地利用模型进行策略学习。最后,通过在OpenAI的Gym公共实验环境和两个工业场景的仿真环境的实验表明:基于二阶价值梯度模型对比传统的基于最大似然估计模型,环境模型预测误差显著降低;基于二阶价值梯度模型的强化学习方法学习效率优于现有的基于模型的策略优化方法,具备更好的控制性能,并减小了控制过程中的振荡现象。可见该方法能有效地提升训练效率,同时提高工业过程控制的稳定性和准确性。