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For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs. [ABSTRACT FROM AUTHOR]

The flotation industrial process is a strong nonlinear and coupling multivariable cascade process with a structure of device and operational layers. To address its operational control problem, this article describes nonlinear dynamic characteristics and disturbances in both device and operational layers as the previous sample unmodeled dynamics and its change rate and proposes a compensation-signal-based dual-rate operational feedback adaptive decoupling control approach. First, a device layer controller consisting of a controller-driven model, a proportional–integral controller, an unmodeled dynamics compensator (UDC), and an unmodeled dynamics change rate compensator (UDCRC) is designed. Then, the device layer closed-loop system and the operational layer system are unified in the same time scale, and hence, a controlled plant model is obtained. A system identification algorithm is proposed to obtain the parameters of this model. With these parameters, an operational layer controller consisting of a controller-driven model, a proportional–integral–derivative controller, a feedback decoupling controller, an UDC, and an UDCRC is designed. Finally, convergence proofs of the proposed identification algorithm and the closed-loop system stability analysis are given, and emulation experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Gaussian processes (GPs) are widely used in surrogate-assisted evolutionary optimization of expensive problems mainly due to the ability to provide a confidence level of their outputs, making it possible to adopt principled surrogate management methods, such as the acquisition function used in the Bayesian optimization. Unfortunately, GPs become less practical for high-dimensional multiobjective and many-objective optimization as their computational complexity is cubic in the number of training samples. In this article, we propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multiobjective and many-objective expensive optimization problems. While the conventional dropout neural network needs to save a large number of network models during the training for calculating the confidence level, only one single network model is needed in the EDN to estimate the fitness and its confidence level by randomly ignoring neurons in both training and testing the neural network. Extensive experimental studies on benchmark problems with up to 100 decision variables and 20 objectives demonstrate that, compared to state of the art, the proposed algorithm is not only highly competitive in performance but also computationally more scalable to high-dimensional many-objective optimization problems. Finally, the proposed algorithm is validated on an operational optimization problem of crude oil distillation units, further confirming its capability of handling expensive problems given a limited computational budget. [ABSTRACT FROM AUTHOR]

This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty. [ABSTRACT FROM AUTHOR]

In this article, a model-free extremum-seeking control (ESC) design is proposed for the operational control of mineral grinding, where both operational indices regulation and throughput maximization are considered. Operational indices regulation and throughput maximization, as two conflicting objectives, are balanced through assigning weighting matrices to obtain a specific choice from the Pareto frontier of the multiobjective optimization problem. To deal with the input constraints, due to the physical limitations and operational constraints, a saturation function is imposed on each input and an extra penalty function is introduced to punish the inputs outside the feasible regions. The integrators and the additive perturbation signals in the ESC are swapped such that dead-zone operators can be added in between and imposed on the estimated gradients to attenuate the oscillation caused by modulating signals. The stability analysis of the proposed control design is provided in the absence of dead-zone operators. A Hardware-in-the-Loop simulation experiment is carried out to show the effectiveness of the proposed ESC. [ABSTRACT FROM AUTHOR]

This brief studies the challenging operational optimization problem of a distillation unit (DU) under varying feedstock properties (e.g., density and carbon content). This problem, in which changes in the feedstock properties are incorporated, aims to quickly obtain the operating variables that control the operating condition of the DU. To solve this problem, we first model this operational optimization problem considering the ever-changing feedstock properties and practical technological constraints. Then, we propose an efficient soft-sensing strategy to rapidly measure the feedstock properties. Finally, motivated by the challenges caused by the varying feedstock properties, product yield, and tray temperature constraints, we propose an optimization algorithm with global search and self-repair capabilities to optimize the operating variables of the DU. The proposed algorithm integrates the optimization time and survival information of each individual into the proposed mutation strategy to improve its global search capability in the irregular feasible region of the operating variables. Based on the ranking and survival information of each individual, the adaptive strategies of the mutation factor and crossover probability are designed to balance the exploration and exploitation capabilities of the optimization algorithm. Subsequently, we propose an effective correction strategy to correct the infeasible operating variables and improve the optimization efficiency of the algorithm. Computational experiments on practical production data show the accuracy of the soft-sensing model and the superiority of the optimization algorithm for operational optimization of the DU. [ABSTRACT FROM AUTHOR]

The dynamic economic optimization of the ultrasupercritical (USC) boiler-turbine unit has become an important task in modern power plants. Economic model predictive control (EMPC) has recently developed to be a promising method for realizing the dynamic economy. This EMPC essentially requires a highly reliable model for USC dynamic prediction which could reflect the internal mechanism of USC with big data feature. This article constitutes a deep-neural-network-based EMPC for the USC unit. Deep belief network (DBN) is used to model the USC unit with mathematical structure. To overcome the nonlinearity and time delay existing in the pulverized channel, an augmented model with predictor embedded is also incorporated into the EMPC design. The auxiliary controller and stability region have been constituted to guarantee closed-loop stability. Simulation results on a 1000-MW USC unit fully demonstrate the effectiveness of the proposed DBN-based EMPC. [ABSTRACT FROM AUTHOR]

This article presents a novel technique to achieve plant-wide performance optimization for large-scale unknown industrial processes by integrating the reinforcement learning method with the multiagent game theory. A main advantage of this technique is that plant-wide optimal performance is achieved by a distributed approach where multiple agents solve simplified local nonzero-sum optimization problems so that a global Nash equilibrium is reached. To this end, first, the plant-wide performance optimization problem is reformulated by decomposition into local optimization subproblems for each production index in a multiagent framework. Then, the nonzero-sum graphical game theory is utilized to compute the operational indices for each unit process with the purpose of reaching the global Nash equilibrium, resulting in production indices following their prescribed target values. The stability and the global Nash equilibrium of this multiagent graphical game solution are rigorously proved. The reinforcement learning methods are then developed for each agent to solve the nonzero-sum graphical game problem using data measurements available in the system in real time. The plant dynamics do not have to be known. Finally, the emulation results are given to show the effectiveness of the proposed automated decision algorithm by using measured data from a large mineral processing plant in Gansu Province, China. [ABSTRACT FROM AUTHOR]

To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity–concavity and connectedness–disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a model-free optimal approach based on reinforcement learning for solving the output regulation problem for discrete-time systems under disturbances. This problem is first broken down into two optimization problems: 1) a constrained static optimization problem is established to find the solution to the output regulator equations (i.e., the feedforward control input) and 2) a dynamic optimization problem is established to find the optimal feedback control input. Solving these optimization problems requires the knowledge of the system dynamics. To obviate this requirement, a model-free off-policy algorithm is presented to find the solution to the dynamic optimization problem using only measured data. Then, based on the solution to the dynamic optimization problem, a model-free approach is provided for the static optimization problem. It is shown that the proposed algorithm is insensitive to the probing noise added to the control input for satisfying the persistence of excitation condition. Simulation results are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In offline data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate (CS) aims to guide the algorithm to quickly find a promising subregion in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the CS. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art offline data-driven multiobjective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes. [ABSTRACT FROM AUTHOR]

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. In this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption. [ABSTRACT FROM AUTHOR]

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners—Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product. [ABSTRACT FROM AUTHOR]

Gaussian processes (GPs) are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because GPs are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing GPs may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a GP and a homogeneous ensemble for infill sampling criteria in evolutionary multiobjective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with GPs and much more scalable in computational complexity to the increase in search dimension. [ABSTRACT FROM AUTHOR]

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization. [ABSTRACT FROM AUTHOR]

Rougher flotation, composed of unit processes operating at a fast time scale and economic performance measurements known as operational indices measured at a slower time scale, is very basic and the first concentration stage for flotation plants. Optimizing operational process for rougher flotation circuits is extremely important due to high economic profit arising from the optimality of operational indices. This paper presents a novel off-policy Q-learning method to learn the optimal solution to rougher flotation operational processes without the knowledge of dynamics of unit processes and operational indices. To this end, first, the optimal operational control for dual-rate rougher flotation processes is formulated. Second, H∞ tracking control problem is developed to optimally prescribe the set-points for the rougher flotation processes. Then, a zero-sum game off-policy Q-learning algorithm is proposed to find the optimal set-points by using measured data. Finally, simulation experiments are employed to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper provides a method for automatically selecting optimal operational indices for unit processes in an industrial plant using measured data and without knowing dynamical models of the unit process. A dynamic multiobjective optimization problem is defined to find operational indices that lead to plant-wide production indices close to their target values. A case-based reasoning (CBR) technique is also employed, which uses the stored experience of a human expert to determine appropriate operational indices for given target production indices. The solutions of the optimization problem and CBR technique are combined to form baseline operational indices. The dynamic models of the production indices, however, are time varying and affected by disturbances and online corrections of these baseline operational indices are required. To this end, reinforcement learning (RL) is used to provide a data-driven optimization technique to compensate for disturbances and model approximation errors and variations. The data-driven RL approach is used in two different time scales. The samples of the predicted production indices are used at a fast sampling rate, i.e., at each sample time, and the samples of actual production indices are used at a slower sampling rate, i.e., after each operational run, to correct the baseline operational indices. The effectiveness of this automated decision procedure has been demonstrated by successful implementation of the proposed approach on a large mineral processing plant in Gansu Province, China. [ABSTRACT FROM PUBLISHER]

Grinding particle size (GPS) of a hematite grinding process (HGP) is characteristic of sensitivity to disturbances of ore hardness and size distribution, difficulty in establishing a mathematic model, and absence of measurement, which make the existing operational optimization and control approaches difficult to be applied. This leads to that it is always difficult to obtain a proper ore feed rate and water flow rate during process operation, and consequently, overload fault often occurs. To tackle this problem, based on characteristic analysis of GPS, a GPS prediction algorithm is first developed using an improved neural network (NN), and a data-driven optimization control approach for safety operation of HGP is proposed. The proposed method adopts a two-layer structure, the higher-level operational feedback control, and lower-level basic loop control. The operational feedback control consists of a cascade NN-based loop setpoint optimizer and an overload diagnosis and self-healing controller. The main advantage of the proposed method is that it requires only the available operating data, without knowing the process dynamics. Experiments have been carried out in a self-developed hardware-in-the-loop experiment system with actual data, demonstrating that the proposed method cannot only keep the HGP operation from mill overload fault but also achieve the optimization control with respect to the quadratic performance index of GPS deviation. [ABSTRACT FROM PUBLISHER]

Grade, due date, priority, and demand are attributes of magnetic material products. Planners are required to seek the optimal combination of production work orders to minimize cost and improve efficiency based on these attributes. The magnetic material group furnace optimization problem is a generalization of the 1-D bin-packing problem wherein bins of varying sizes are used. Bin sizes are determined by the grade and demand of the grouped work orders. A mathematical model is established to solve the magnetic material group furnace optimization problem by using a specialized genetic algorithm (SGA). In SGA, an initial population generation method is designed by following the sort criteria of the earliest completion date. The furnace charging weight is set according to several rules derived from work order attributes. An elite strategy and an improved greedy three-crossover operator are introduced to enhance convergence speed and precision. In addition, a reverse operator is applied to exploit the proposed algorithm. Simulation results based on practical production data show that the established model is suitable and that the presented algorithm is effective. [ABSTRACT FROM PUBLISHER]

The whole production line of hematite ore processing is composed of raw ore processing, shaft furnace roasting, grindings, and magnetic separation production phases. Their automation systems consist of the process control part and the operational optimization system. The target of the optimal operational control is to optimize the concerned operational indices, namely, the intermediate product quality, efficiency, and consumptions. The dynamics between the operational indices and the global production indices (i.e., the total concentration grade, metal recovery rate, production rate, beneficiation ratio, and costs) with month, day, and hour time scales changes in line with the variations of production conditions, composition of raw ore together with capability of equipment. These indices are difficult to measure online and as a result it is difficult to model accurately. Moreover, there are characteristics in terms of both interconnections and conflictions among these indices. This leads to isolated operation of individual automation systems for these processes and the optimization of global production indices for whole production line cannot be realized. This paper presents a novel problem description for the integrated optimization of the automation systems of mineral processing. For this purpose, the analysis is made on the difficulty of using the existing optimization methods-based decision making methods to obtain the integrated optimization of the automation systems. The integrated optimization strategy for the automation systems of mineral processes is proposed using our previously established target value optimization of global production indices , two time scales decomposition approach and target value optimization of operational indices. The proposed strategy aims at realizing the optimization of global production indices. Using real data from a mineral processing plant on hematite beneficiation process, relevant simulations, and real industrial experiments have been carried out. The obtained experimental results show the efficiency and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. [ABSTRACT FROM AUTHOR]

This paper investigates the setpoints compensation for a class of complex industrial processes. Plants at the device layer are controlled by the local regulation controllers, and a multirate output feedback control approach for setpoints compensation is proposed such that the local plants can reach the dynamically changed setpoints and the given economic objective can also be tracked via certain economic performance index (EPI). First, a sampled-data multivariable output feedback proportional integral (PI) controller is designed to regulate the performance of local plants. Second, the outputs and control inputs of the local plants at the device layer are sampled at operation layer sampling time to form the EPI. Thus, the multirate problem is solved by a lifting method. Third, the static setpoints are generated by real-time optimization and the dynamic setpoints are calculated by the compensator according to the error between the EPI and objective at each operation layer step. Then, a networked case is studied considering unreliable data transmission described by a stochastic packet dropout model. Finally, a rougher flotation process model is employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Optimizing the final grinding production indices (GPIs), which include the product particle size and the grinding production rate, to meet the overall manufacturing performance requirements is the main function of automatic control of a grinding circuit (GC). However, the complex and time-varying nature of the GC process dictates that these GPIs cannot be optimized solely by the lower-level distributed control systems (DCS), therefore an operator is often incorporated to manually determine the set-points for the DCS using his/her operational experience. With a human being involved, the performance and even the safety and stability of the GC operation is subject to human errors. Focusing on this practical challenge, this paper proposes an intelligence-based supervisory control strategy that consists of a control loop set-point optimization module, an artificial neural network-based soft-sensor module, a fuzzy logic-based dynamic adjustor, and an expert-based overload diagnosis and adjustment module to perform the control tasks for the GC system. This hybrid system can automatically adjust the set-points for the DCS-controlled grinding system in response to the changes in boundary conditions or the imminent overload conditions, thereby eliminating the need for an operator. Practical applications have shown the validity and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Complex industrial processes are controlled by the local regulation controllers at the field level, and the setpoints for the regulation are usually made by manual decomposition of the overall economic objective according to the operators' experience. If a precise static process model can be built, real-time optimization (RTO) can be used to generate the setpoints. Nevertheless, since the aforementioned control structure is actually open-loop, the desired economic objective of the whole processes may not be tracked when disturbances exist. Aiming at solving this problem, a novel network based model predictive control method (MPC) for setpoints compensation is proposed in this paper. Firstly, a multivariable proportional integral (PI) controller is designed to perform the local regulation control. Secondly, a stochastic packet dropout model is adopted to characterize the measurement and human-in-the-loop delay effect. Then, a model predictive controller considering the random dropout effect is developed to compensate the setpoints dynamically according to the changing conditions of the processes, such that the prescribed performance objective can be obtained. Finally, a flotation process model is employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, a novel knowledge-based global operation approach is proposed to minimize the effect on the production performance caused by unexpected variations in the operation of a mineral processing plant subjected to uncertainties. For this purpose, a feedback compensation and adaptation signal discovered from process operational data is employed to construct a closed-loop dynamic operation strategy. It uses the signal to regulate the outputs of the existing open-loop and steady-state based system so as to compensate the uncertainty in the steady-state operation at the plant-wide level. The utilization mechanism of operational data through constructing increment association rules is firstly described. Then, a rough set based rule extraction approach is developed to generate the compensation rules. This includes two steps, namely the determination of the variables to be compensated based on the significance of attributes in the rough set theory and the extraction of the compensation rules from process data. Based upon the operational data of the mineral processing plant, relevant rules are obtained. Both simulation and industrial experiments are carried out for the proposed global operation, where the effectiveness of the proposed approach has been clearly justified. [ABSTRACT FROM PUBLISHER]

The production planning optimization for mineral processing is important for non-renewable raw mineral resource utilization. This paper presents a nonlinear multiobjective programming model for a mineral processing production planning (MPPP) for optimizing five production indices, including its iron concentrate output, the concentrate grade, the concentration ratio, the metal recovery, and the production cost. A gradient-based hybrid operator is proposed in two evolutionary algorithms named the gradient-based NSGA-II (G-NSGA-II) and the gradient-based SPEA2 (G-SPEA2) for MPPP optimization. The gradient-based operator of the proposed hybrid operator is normalized as a strictly convex cone combination of negative gradient direction of each objective, and is provided to move each selected point along some descent direction of the objective functions to the Pareto front, so as to reduce the invalid trial times of crossover and mutation. Two theorems are established to reveal a descent direction for the improvement of all objective functions. Experiments on standard test problems, namely ZDT 1–3, CONSTR, SRN, and TNK, have demonstrated that the proposed algorithms can improve the chance of minimizing all objectives compared to pure evolutionary algorithms in solving the multiobjective optimization problems with differentiable objective functions under short running time limitation. Computational experiments in MPPP application case have indicated that the proposed algorithms can achieve better production indices than those of NSGA-II, T-NSGA-FD, T-NSGA-SP, and SPEA2 in the case of small number of generations. Also, those experimental results show that the proposed hybrid operators have better performance than that of pure gradient-based operators in attaining either a broad distribution or maintaining much diversity of obtained non-dominated solutions. [ABSTRACT FROM AUTHOR]

The 17 papers in this special section focus on integrated optimization of industrial automation. [ABSTRACT FROM AUTHOR]