Software platform for optimal setting control of 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.

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 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.

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).

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.

Optimal-setting control for complicated industrial processes and its application study

In modern industrial field, some complex processes have multi subprocesses, which are homo-structural variable-parameter systems and have quite a few control parameters. When the subprocesses have nonlinear characteristics, it is difficult to turn these control parameters manually. To solve this problem, a distributed immune algorithm for controlling the complex industrial process is designed on the basis of theory of immune network, which is able to learn control knowledge of different operators online and optimize the existing control knowledge by immune evolution and learning in terms of control results. Experiments and simulations show that it has the ability of distributed learning and its control results are superior to that of the manual. The algorithm can solve the problem of intelligent control for homo-structural variable-parameters systems in complex industrial process.

Dynamic programming (DP) is an elegant way to solve problems related to optimization and optimal control of processes. DP, however, has one major drawback, namely the “curse of dimensionality”. To overcome this shortcoming, an approach called neuro-dynamic programming (NDP) has been developed. This approach solves the “curse of dimensionality” problem of DP. For this purpose, a neural network is used in NDP, which ignores the poor results of the utility criterion. In this way, the time for solving the specific task is significantly shortened. In this work, an NDP algorithm is presented for the optimal control of a fed-batch biotechnological process for the production of L-lysine by the strain Brevibacterium flavum 22LD. Application of the NDP algorithm ensures maximum productivity of the L-lysine.

In different branches of economics-especially in the construction industry - supply systems are found, in which producers and consumers are connected by production and transport. By the occurrence of random or foreseeable perturbations an optimal control of the supply processes is appropriate. First directed graphs are assigned to special supply systems. On there graphs multistage decision processes are defined to yield an optimal control of the supply processes. The explicit representation of the perturbing quantities by parameters is decisive for optimal control. Next, an optimization problem is formulated, which includes as a special case the problem of optimal control of supply processes. This mathematical modelling and the solution of the optimization problems require some extensions of the known theory of multistage decision processes.

Optimal control of an industrial-scale evaporation process: Sodium aluminate solution

Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network

Three layer control structure is proposed for optimal control of continuous fermentation processes. The start-up optimization problems are solved as a first step for optimization layer building. A steady state optimization problem is solved by a decomposition method using prediction principle. A discrete minimum time optimal control problem with state delay is formulated and a decomposition method, based on an augmented Lagrange's function is proposed to solve it. The problem is decomposed in time domain by a new coordinating vector. The obtained algorithms are used for minimum time optimal control calculation of Baker's Yeast fermentation process.

Some Recent Results in the Optimal Control of Diffusion Processes