A distributed immune algorithm for learning experience in complex industrial process control

Nowadays, research and development for optimal setting control (OSC) of complex industrial processes is becoming a hot topic. However, there is no configurable software for OSC design. In this paper, a novel software platform is proposed. The major superiority is that it not only integrates intelligent and data-driven mainstream algorithms within a unified framework, but also provides a powerful controller design tool and employs a Petri net engine to schedule control units in a parallel computation environment. The overall framework and key technologies are introduced in detail. The platform is verified and validated through a case of application to the design and development of OSC system of grinding process in a hardware-in-the-loop simulation environment.

A central challenge in the control of complex industrial processes like heat exchangers is the persistent disconnection between global offline optimization and real-time online adaptation, leading to suboptimal performance under dynamic conditions. To address this, this paper proposes a coordinated hierarchical control strategy, CB-BPP, a three-layer framework integrating Chaotic Particle Swarm Optimization (CFPSO), a Backpropagation Neural Network (BPNN) identifier, and a Predictive Functional Control (PFC)-based PID controller. At the top layer, CFPSO performs offline global optimization of the BPNN's initial weights and establishes safe bounds for controller parameters. The middle layer employs the BPNN for online system identification and real-time adaptation of PID parameters. The bottom layer executes high-precision control. This tiered architecture decouples offline optimization from online adaptation, enabling synergistic control. Comprehensive simulations demonstrate the superiority of CB-BPP, reducing the Integral of Absolute Error (IAE) by up to 45% and improving recovery speed by 35% compared with advanced baseline methods. Furthermore, extensive robustness analysis against sensor noise and parameter drift, alongside a successful Hardware-in-the-Loop (HIL) implementation, validates its practical applicability. The results confirm the proposed method's high precision, strong robustness, and real-time feasibility, providing an advanced control solution for complex industrial processes.

The operation control for complex industrial processes consists of two layer — loop control layer and operational control layer. The former aims at achieving the required loop control for each production unit along production line whilst the later optimizes the set-points to the control loops so that certain performance indexes (such as product quality and energy cost) is optimized when the closed loop controlled variables follow their set-points well. This paper presents a novel method that can be used to analyze the performance deterioration of the optimized operational control, where the impact of tracking errors of loop control to the optimized performance is quantitatively formulated when the tracking errors are small. It has been shown that loop tracking errors would generally deteriorate the optimized performance in a quadratic way — leading to the establishment of the square impact principle (SIP). Moreover, it has been shown that the production infrastructure will also affect the deterioration of the optimized performance. Formulation on the analysis on the flat robustness (FR) and randomness of the optimized performance indexes will also be made and several issues on the future studies are listed.

Artificial intelligence algorithms demonstrate exceptional potential for decision optimization in adaptive control of complex industrial processes. Addressing nonlinear coupling, signal fluctuations, and operational uncertainties in sand and gravel crushing processes, this study employs reinforcement learning to construct a multimodal perception-driven adaptive control framework. The objective is to achieve dynamic adjustment of crushing parameters and energy consumption optimization through intelligent algorithms, thereby enhancing system stability and precision. Methodologically, Deep Reinforcement Learning (DRL) integrates signal and image features to construct the state space. Based on the DQN algorithm, state perception, reward functions, and policy network structures are designed. Adaptive learning rates and online update mechanisms are introduced to enable real-time iteration of control strategies. Results demonstrate that during experimental platform validation, this model significantly reduces crushing particle size error by 57.3%, lowers energy consumption by 29.3%, and shortens response time by 46.4% compared to traditional PID and fuzzy control methods, achieving rapid convergence and stable control under complex operating conditions. Conclusions indicate that the reinforcement learning-based adaptive control architecture effectively enhances the intelligence level of sand and gravel crushing systems, providing an extensible and transferable algorithmic pathway for industrial process control with broad engineering application prospects.

Actually, there is a trend of integrating simulation based components in enterprise information and decision support systems. The available data in production planning and control software systems can be used to construct the models, the objective-oriented realisation of simulation experiments and the economic evaluation of the results. In this context, the conception and realisation of integrated systems is very important for the analysis, modelling, simulation and optimisation of business processes. The paper describes an approach of the production scheduling problem under consideration of high complex process conditions. The new control strategies are based on hierarchical concepts. Local and global mechanisms are used for the order scheduling. These methods are integrated in a software system for short time production planning and control.

This paper presents a multi-agent system for multivariate process monitoring. The proposed multi-agent system combines several intelligences which are: multivariate control charts, neural networks, Bayesian networks, and expert systems. This system aims to realise a complete control of complex industrial process. In order to demonstrate the efficiency of the proposed multi-agent system, it has been applied and evaluated in the monitoring of the complex process Tennessee Eastman process (TEP).

There are many complex industrial processes with highly nonlinear and uncertain characteristics which are generally difficult to control by well known PID controllers. Active disturbance rejection control (ADRC), a relatively recent nonlinear control method, is an alternative framework which has the advantages on simple algorithm, strong robustness, and less dependence on the mathematical models of the plant. However, owing to the large number of free control parameters for ARDC, the design is not straightforward, i.e., the proper state observer gains are mostly done on a trial and error basis. In this paper, a genetic based new design scheme is proposed, and a digital control solution to super-heated steam temperature regulation of a thermal power plant is presented. For comparison purposes, a PID cascade controller is considered as a base line. The simulation experiments of robustness tests and disturbance rejection tests have proved that the proposed method has outstanding adaptability to the variety of operating regime, can effectively estimate and compensate the plant perturbations of parameters and external disturbances in the regulation system.

Aiming at the defect traceability challenges caused by the complexity of modern industrial process operation mechanisms and high-dimensional parameter coupling, a quality anomaly diagnosis framework integrating interpretable machine learning and knowledge graph is proposed. The quantitative contributions of process parameters to quality indicators are assessed by constructing an architecture combining XGBoost prediction model and SHAP (SHapley Additive exPlanation) interpretable analysis. Structured storage of variable association patterns of historical data based on a knowledge graph. The K Nearest Neighbors (KNN) algorithm is further introduced to construct a data matching mechanism to achieve causal traceability of real-time anomalies through similarity-driven historical defect retrieval. The experimental results show that the method has a good ability to identify the root cause parameters of defects under multiple working condition scenarios, and provides a solution with both data-driven characteristics and knowledge interpretability for the quality control of complex industrial processes.

URLLC is the key feature of 5G enabling extensive V2X applications and control of complex industrial processes via wireless communication systems. In order to ensure the highest reliability requirements with low latency communication, the fluctuating characteristics of a wireless link must not become a limiting factor for the use case. Cooperative communication approaches eliminate this problem by exploiting additional links via relay nodes to transmit data redundantly on different paths in the network to the destination node. We present a suitable multi-path scheme for integration in 5G and B5G systems. The theoretical analysis shows nearly one order of magnitude improvement of reliability for each additional path with the proposed architecture. Furthermore, we demonstrate a significant gain in terms of reliability for the overall link even for highly disrupted relay paths. Using experimental vehicle platooning measurements in real-world traffic based on LTE and IEEE802.11p, we confirm the conceptual advantage of our multi-path approach.

A complex industrial process is described by means of a new model of process/transition Petri nets with mixed-mark, namely time-varied process/transition Petri nets in this paper. In this model, the running marks and remnant time of sub-process are introduced. Based on logic and timed mark variables, it solved effectively many problems of complex industrial process, such as the integrated modeling of sub- process, the coupling between discrete variables and continuous states and joining between process controls and production control. And real-time supervisory control and scheduling of complex industrial process with multi-working places are studied deeply based on this model. Finally, the approaches to solve optimal strategies of real-time supervisory control, the realtime scheduling and process control are proposed.

Present paper attempts to review the literature related to design of P-I-D control for time delayed complex industrial process for single as well as for multivariable process with interaction considerations, their decoupler design and time delay compensators. General instrumentation of the industrial feedback control systems along with control system analysis has been covered. Also it covers, control features of some paper mill sub-processes like headbox operation, basis weight and retention. The importance of eliminating the effects of interactions, among the process control loops inside a multi input multi output industrial control system, has been discussed with the help of literature study. The importance of the process dynamics knowledge for designing a control system has also been investigated. This paper also investigates the significance and effectiveness of PID controllers through various literature studies. The problems of the classical PID controllers such as constraints, presence of disturbances etc.) can be removed by designing in combination with soft computing techniques. Moreover, possibility of further enhancements in the PID controller with the utilization of various schemes available, has been presented. The present status of control systems in industrial processes in terms of various control parameters such as stability, dead time compensation etc. has been presented and the future improvements have been stated.

In today's complex industrial processes, automated control systems are a necessity. However, is complete automation the answer? Whereas control system automation provides predictable, consistent performance, it is lacking in human judgment, adaptability, and logic. Although humans provide these, we are unpredictable, inconsistent, and subject to emotions and motivation. To maximize system performance, should we automate humans out of the system? … or … Do we maximize human input and lose efficient, consistent, error‐free system performance? The answer is likely somewhere in the middle of these two extremes and different for each system and situation. This paper provides a review of the existing literature covering control schemes and parameters that determine system performance. It attempts to help answer the questions “How can we minimize human error while still maximizing system performance?” and “What is the right human–machine mix?” © 2005 American Institute of Chemical Engineers Process Saf Prog, 2005

On-Line Trajectory Encoding for Discrete-Observation Process Monitoring

Steelmaking with a Linz-Donawitz converter is a complex industrial process, where, due to the lack of exact mathematical (physical-chemical) models, the construction of a black-box model based on noisy and imprecise data is required. To construct a good model, a large number of such input-output samples should be used, which calls for a method that is sparse, in the sense that the resulting model complexity is independent of the sample number, and robust to reduce the effects of noise. Lately, support vector machines (SVMs) have successfully been applied to a number of such problems. The main problem with traditional SVM is its high algorithmic complexity, which makes it infeasible for really large databases. The least-squares SVM (LS-SVM) solves this problem, but the resulting model is not sparse. Our solution uses a sparse and robust extension of LS-SVM that leads to good results compared to other methods (such as MLPs) applied to the same problem.

Hydrothermal stagnation point flow of Carreau nanofluid over a moving thin needle with non-linear Navier's slip and cubic autocatalytic chemical reactions in Darcy-Forchheimer medium