Process Control Problems Research Articles

Enhance the Uncertainty Modeling Ability of Fuzzy Grey Cognitive Maps by General Grey Number

In real-life systems, people cannot get precise data. The data are represented in the forms of interval or multiple intervals in many cases. Most intelligent algorithms are designed for precise data in algorithm research. People always use a real number contained in the interval or multiple intervals as the candidate for the precise data. However, such a measure will lose lots of information contained in the interval or multiple intervals. Fuzzy Cognitive Map (FCM) is one of the famous intelligent algorithms. The Fuzzy Grey Cognitive Map (FGCM) was proposed to enable FCM to do interval computation. But FGCM can only deal with a single interval, as for multiple intervals, the FGCM is powerless. Thus, this paper aims to enhance the FGCM to make it can cope with multiple intervals. The paper introduces the general grey number and deduces the new activation functions according to Grey System Theory (GST) and Taylor series. Finally, an industrial process control problem is applied to verify the new algorithm. The results show that the new algorithm is not only compatible with the original FGCM and FCM, but can process more uncertain knowledge and data. In general, the new algorithm inherits most of FGCM's characteristics and can cope with the data expressed by multiple intervals, which means it can be used in environments with more uncertain knowledge and data.

The Use of a Semi-Rigorous SAG Mill Model for a Hands-On Workshop

This paper describes the extension of a grinding mill model that is publicly available for educational and training purposes. This industrially derived model of a mill is used to explain complex concepts of advanced process control (APC) such as model predictive control and soft sensing. The resulting concepts were developed in a hands-on workshop in the wake of the IFAC MMM 2019 conference entitled “Modern Data Analytics for Control in Minerals Processing”. The workshop and the material presented here are aimed at control engineers to explain the use of APC methods to improve process performance. Both academic and industrial control engineers attended the workshop, highlighting the relevance of the application of real-life process control problems.

An optimal control problem for a Wiener process with random infinitesimal mean

We consider a stochastic optimal control problem for one-dimensional diffusion processes with random infinitesimal mean and variance that depend on a continuoustime Markov chain. The process is controlled until it reaches either end of an interval. The aim is to minimize the expected value of a cost criterion with quadratic control costs on the way and a final cost. A particular case with a Wiener process will be treated in detail. Approximate and numerical solutions will be presented.

An efficient charting scheme for multivariate categorical process with a sparse contingency table

Multivariate categorical quality characteristics, whose distribution can be displayed by a contingency table, are routinely encountered in many applications. When most of the cell entries in the contingency table are very small or zeros counts, which is so-called sparse contingency table in the literature, existing methods developed in the literature are often inadequate for use, due to the inaccuracy of the maximum likelihood estimate of its probability distribution, and the inflation of online charting statistics. This paper studies the multivariate statistical process control problem for such sparse contingency table. We integrate the group least absolute shrinkage and selection operator (LASSO) method with the Ridge method to estimate the in-control distribution of a contingency table and propose an efficient EWMA control chart, based on a modified Pearson χ2 statistic, to monitor the changes in it. Numerical results show that our proposed approach has the best overall performance, compared with its competitors. Finally, a real data example is used to demonstrate the effectiveness of the proposed control chart.

On an optimization method of chemical reactors

The class of model problems of optimal process control of chemical reactors is considered. A new method of constructing successive approximations to the optimal problem solution is proposed, based on the implementation of the constructed conditions for improving control in the form of fixed-point problems. This form allows to apply and modify the theory and methods of searching for fixed points to optimize control. A comparative analysis of the effectiveness of the proposed optimization method with well-known computational algorithms in the calculation of specific model problems of optimization of chemical reactors is carried out.

Adaptive Iterative Control of Temperature in Greenhouse

Introduction. Creation and development of efficient agricultural complexes providing high yields at minimal time, material, and energy costs is impossible without the use of automatic control systems (ACS), which allow for maintaining the microclimate of the greenhouse with high accuracy. Improvements of the microclimate by ASC are aimed at neutralizing the influence of parametric perturbations of processes inside and outside the greenhouse. Using the example of a temperature control channel in a greenhouse with a heating circuit based on hot piped water supply, an adaptive iterative (search) algorithm for adjusting the components of a proportional-integral-differential (PID) controller in the heating circuit is proposed to ensure the required quality of the control process. Materials and Methods. The management is based on a parametrically uncertain model of temperature in the greenhouse, the structure of which, based on the principle of superposition, is transformed into a form with control and disturbances concentrated on an output coordinate. The use of an adaptive PID controller is based on the real-time analysis of a database containing trends of the controlled process. Using operators of Database Managment System or SQL language, queries evaluate regulation quality in accord with quality assessment. Proportional and differential components of the PID controller are adjusted so that the control system works on the verge of switching to auto-oscillation mode. The resulting static error is compensated by a change in the driving force. Results. Simulation of the real structure of a single-circuit automatic control system with temperature in the greenhouse with built-in regulating, executive and measuring elements, with a delay of a coolant movement was carried out using the software MVTU (SimIn- Tech). The proposed adaptation algorithm, consisting of the additive adjustment of the PID controller coefficients and being conveniently implemented within the SCADA system, was shown to provide the minimum oscillatory temperature maintenance for arbitrary parametric perturbations in the presence of the delay. Discussion and Conclusion. The proposed adaptation algorithm provides for compensation for model uncertainty and disturbances, while achieving the required accuracy of maintaining the temperature in the greenhouse. Results of the study will serve as the basics for development of multi-contour ACS microclimate greenhouses with the examination of the impact and compensation of parametric and structural uncertainty, inertia and nonlinearities of real elements. Our results may be used in many sectors of the national economy to study the general and applied problems of digital adaptive process control.

Process Control Decision Inference, Monitoring, and Execution

Approaches for inferring process control system decision trees from plant data have been heavily researched and demonstrated, but the utility of applying such decision tree inferences for autonomously monitoring, guiding, and executing control philosophies has been lacking. The authors have implemented an architecture leveraging model-based reasoning and graphical programming that investigates the utility of using control decision tree inferencing for validating, monitoring, and executing control strategies for both simple and complex process control problems. The techniques are potentially useful for assessing process control health, as well as extracting process control knowledge that may exist in daily operations but not recognized or well understood by analysts and management. Plant operators and managers can readily employ such insights for improving, augmenting, and extending process control system behavior, but can also potentially employ the refined and validated decision trees as a supervisory control layer on top of their existing control systems.

STUDY OF THE GEOMETRY OF GRINDING MACHINES USED FOR LARGE SCALE CRANKSHAFT MACHINING

In the paper, grinding process control problems that occur during the marine diesel engine crankshaft production are addressed. The large size crankshafts have a length of up to 12 m and weigh up to 25 tons, but the final grinding allowance for the diameter is ca. 0.3-0.4 mm. To achieve this, very high accuracy of the mechanical parts of the grinding machine is necessary. The study focused on the measurement of parameters such as level, linearity, parallelism, runout and coaxiality of the respective mechanical parts of the grinding machine. Based on the results, some recommendations were made on the inspection procedure in order to ensure a consisted quality of the produced crankshafts. The obtained roughness parameters after grinding were found highly satisfactory, allowing effective polishing afterwards.

Robust predictor based control of state multiplicative noisy retarded systems

Linear, continuous-time systems with stochastic uncertainties and delayed input are considered. The problems of H∞ state-feedback control and static output-feedback control are solved, for nominal systems, applying a Luenberger-type predictor. The two problems are solved via the input–output approach, where the system is replaced by a non-retarded system with deterministic norm-bounded uncertainties. The cost function in these two problems is the expected value of the standard H∞ performance index with respect to the stochastic parameters.The above problems are solved also for the case where the deterministic parameters of the system encounter either norm-bounded or polytopic-type uncertainties. In the uncertain state-feedback control problem the solution is obtained by applying a vertex-dependent approach that is based on the Finsler lemma. An illustrative example is given that compares several control solution methods. An additional example of practical process control problem is also solved.

Time-optimal symbolic control of a changeover process based on an approximately bisimilar symbolic model

Many process control problems with complex qualitative specifications cannot be addressed via conventional control design methods. Examples of such specifications include logic specifications expressed in the design of start-up, shut-down, changeover, and emergency shutdown operating procedures. In recent years, it has been shown in the control systems and computer science communities that symbolic models provide convenient and powerful mechanisms to synthesize controllers enforcing such qualitative specifications. The use of symbolic models reduces the synthesis of the controllers to a fixed-point computation problem over a finite-state abstract system. In this paper, after explaining the notion of approximate bisimulation for incrementally globally asymptotically stable (δ-GAS) nonlinear control systems, the construction of approximately bisimilar symbolic models for such systems is presented. Then synthesis of time-optimal symbolic controller for this class of systems is performed based on results from the computer science literature. As a benchmark chemical process control problem, an approximately bisimilar symbolic model is constructed for a safe changeover process. Then a symbolic controller is designed and it is refined to a controller to be applied to the original process. Simulation results show the effectiveness of the symbolic controller. Although the construction of the symbolic model may be complex, the synthesis of the controller in a finite-state space is fast and most importantly the error bounds are adjustable as design parameters.

Application of Improved Fuzzy-Smith Controller in the Control System of Cement Rotary Kiln

Rotary cement kiln is a large time delay and inertia component. It is a typical problem in industrial process control, so advanced control methods should be adopted. This paper designs an improved Fuzzy-Smith controller. It combines Fuzzy with improved Smith predictor control method. Smith predictor algorithm compensates for the time delay and Fuzzy control is used to enhance the robustness of the system. The simulation results show that the improved Fuzzy-Smith controller owns stronger robustness and much better control quality. And it is an appropriate and simple control method for the system with pure lag.

Educational Methodology Based on Active Learning for Mechatronics Engineering Students: towards Educational Mechatronics

The world is experiencing a new stage, the Fourth Industrial Revolution or also called Industry 4.0, which promotes the digital revolution of industries through the use of artificial intelligence and the internet of things; this revolution will generate new jobs. This work proposes the creation of Educational Mechatronics, which is an educational methodology that aims to develop the skills and abilities requiredby the jobs of the new industrial era by promoting the analytical thinking as a previous step for wardcritical thinking competence, allowing to generate solutions and innovative proposals for problems of industrial automation and automatic process control that companies from the industrial sector experience nowadays. The educational mechatronics integrates the Educational Model of the University, resourcesand existing academic spaces, practical activities and mechatronic prototypes focused on promoting the critical thinking of Mechatronic Engineering students through active learning. In addition, it is sought that with the inclusion of the Educational Mechatronics the levelof attraction of the students for their career will be increased by having an intensive active participation in experimental sessions and thus reduce the percentage of student desertion. The fore coming engineers need tobe trained with an innovative educational methodology since they will be in charge of strengthening and promoting the next technological revolution Industry 4.0, promoting new business and the social and economic development of the countries.

Optimal liquidation under partial information with price impact

We study the optimal liquidation problem in a market model where the bid price follows a geometric pure jump process whose local characteristics are driven by an unobservable finite-state Markov chain and by the liquidation rate. This model is consistent with stylized facts of high frequency data such as the discrete nature of tick data and the clustering in the order flow. We include both temporary and permanent effects into our analysis. We use stochastic filtering to reduce the optimal liquidation problem to an equivalent optimization problem under complete information. This leads to a stochastic control problem for piecewise deterministic Markov processes (PDMPs). We carry out a detailed mathematical analysis of this problem. In particular, we derive the optimality equation for the value function, we characterize the value function as continuous viscosity solution of the associated dynamic programming equation, and we prove a novel comparison result. The paper concludes with numerical results illustrating the impact of partial information and price impact on the value function and on the optimal liquidation rate.

An Example of Multi-objective Optimization for Dynamic Processes

Recent research activity has shown an interest in multi-objective optimization of dynamic processes, both for design and for real-time control; see Liu et al. (2018), Valderrama & Ruiz (2018), and de Sousa Santos et al. (2018) for some recent examples. Many of the methods proposed for these problems are based on converting the multi-objective problem into a single objective through the use of weights. Some works, for instance, Liu et al. (2018), however, consider the multi-objective problem directly but even in these cases, the problem is treated as a single objective problem with other objectives incorporated as constraints. All of these methods may not necessarily generate a satisfactory trade-off or Pareto optimal curve when this trade-off curve is non-convex. In this paper, we illustrate the use of a plant propagation nature inspired algorithm for the solution of design and control problems for dynamic processes. Being population based, it is able to identify an approximation to the Pareto optimal frontier simultaneously. A simple example from the literature is used to demonstrate how this problem can be formulated. The open source, freely available, Fresa application (http://www.ucl.ac.uk/~ucecesf/fresa.html),written in Julia, is used and the discussion concentrates on the problem formulation. The result shows how a multi-objective approach may lead to better insight into the design or control issues. The illustrated problem was originally formulated as a single objective problem. In doing so, information about alternative designs and how these could affect the performance of the system may be hidden. A multi-objective formulation allows the engineer to make better informed design decisions.

Water Pump Control: A Hybrid Data-Driven and Model-Assisted Active Disturbance Rejection Approach

Water pump control, prevalent in various industrial plants, such as wastewater treatment and steam generator facilities, plays a significant role in maintaining economic efficiency and stable plant operation. Due to its slow dynamics, strong nonlinearity, and various disturbances, it is also widely studied as a typical benchmark problem in process control. The current control strategies can be categorized into two aspects: one branch resorts to model-based design and the other to data-driven design. To merge the merits and overcome the deficiencies of each paradigm, this paper proposes a hybrid data-driven and model-assisted control strategy, namely modified active disturbance rejection control (MADRC). The model information regarding water dynamics is incorporated into an extended state observer (ESO), which is used to estimate and mitigate the limitations of slow dynamics, strong nonlinearity, and various disturbances by analyzing the real-time data. The tuning formula is given in terms of the desired closed-loop performance. It is shown that MADRC is able to produce a satisfactory control performance while maintaining a low sensitivity to the measurement noise under general parametric setting conditions. The simulation results verify the clear superiority of MADRC over the proportional-integral (PI) controller and the conventional ADRC, and the results also evidence its noise reduction effects. The experimental results agree well with the simulation results based on a water tank setup. The proposed MADRC approach is able to improve the control performance while reducing the actuator fluctuation. The results presented in this paper offer a promising methodology for the water control loops widely used in the water industry.

Intelligently Informed Control Over the Process Variables of Oil and Gas Equipment Maintenance

This article details a new technique that uses intelligent methods in order to identify non-standard errors when controlling the technological process of maintenance petroleum equipment. First, a new formulation of the technological process control problem for the maintenance of petroleum equipment is presented. This is stated in terms of classifying errors introduced by measuring the parameters of the technological process. Intelligent methods have been proven as a tool to solve the classification problem. Various machine-learning methods have been considered: decision trees, artificial neural networks (ANN), and fuzzy logic. In this study, an effectiveness comparison of the proposed methods has been conducted using experimental data of petroleum equipment maintenance. Results indicate that ANN is the most efficient method to classify measurement errors. The proposed method will primarily improve repair quality of certain equipment components such as the pipeline system for transferring raw hydrocarbon materials. Moreover, it will improve the quality of maintenance work and durability of the pipeline system, which in turn can increase the hydrocarbon production efficiency.

Optimal Control of the Separation Process with Flow Restrictions

Here we formulate the problem of optimal process control with distributed parameters, taking into account the constraints on the control and associated flows. The necessary optimality conditions are obtained. The analysis of the stationarity conditions is carried out and the method for constructing the domain of admissible controls is proposed. The developed optimization method is applied in the automation of industrial distillation plants for the sulfuric acid alkylation of isobutane with butylenes, ortho-xylene production, etc.

One‐sided control chart based on support vector machines with differential evolution algorithm

AbstractThe statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one‐sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non‐normal distribution cases. The ARL performance of the proposed chart is better than the distance‐based SVM chart. A real example is used to illustrate the application of the proposed control chart.

Adaptive Strategic Cyber Defense for Advanced Persistent Threats in Critical Infrastructure Networks

Advanced Persistent Threats (APTs) have created new security challenges for critical infrastructures due to their stealthy, dynamic, and adaptive natures. In this work, we aim to lay a game-theoretic foundation by establishing a multi-stage Bayesian game framework to capture incomplete information of deceptive APTs and their multistage multi-phase movement. The analysis of the perfect Bayesian Nash equilibrium (PBNE) enables a prediction of attacker's behaviors and a design of defensive strategies that can deter the adversaries and mitigate the security risks. A conjugate-prior method allows online computation of the belief and reduces Bayesian update into an iterative parameter update. The forwardly updated parameters are assimilated into the backward dynamic programming computation to characterize a computationally tractable and timeconsistent equilibrium solution based on the expanded state space. The Tennessee Eastman (TE) process control problem is used as a case study to demonstrate the dynamic game under the information asymmetry and show that APTs tend to be stealthy and deceptive during their transitions in the cyber layer and behave aggressively when reaching the targeted physical plant. The online update of the belief allows the defender to learn the behavior of the attacker and choose strategic defensive actions that can thwart adversarial behaviors and mitigate APTs. Numerical results illustrate the defender's tradeoff between the immediate reward and the future expectation as well as the attacker's goal to reach an advantageous system state while making the defender form a positive belief.

Constrained BSDEs Driven by a Non-Quasi-Left-Continuous Random Measure and Optimal Control of PDMPs on Bounded Domains

We consider an optimal control problem for piecewise deterministic Markov processes (PDMPs) on a bounded state space. A pair of controls acts continuously on the deterministic flow and on the two transition measures (in the interior and from the boundary of the domain) describing the jump dynamics of the process. For this class of control problems, the value function can be characterized as the unique viscosity solution to the corresponding fully nonlinear Hamilton--Jacobi--Bellman equation with a nonlocal type boundary condition. By means of the recent control randomization method, we are able to provide a probabilistic representation for the value function in terms of a constrained backward stochastic differential equation (BSDE), known as the nonlinear Feynman--Kac formula. This result considerably extends the existing literature, where only the case with no jumps from the boundary is considered. The additional boundary jump mechanism is described in terms of a non-quasi-left-continuous random measure and induces predictable jumps in the PDMP's dynamics. The existence and uniqueness results for BSDEs driven by such a random measure are nontrivial, even in the unconstrained case, as emphasized in the recent work [E. Bandini, Electron. Commun. Probab., 20 (2015), pp. 1--13].