Multiobjective process line optimization under uncertainty applied to papermaking

Madetoja, Elina; Tarvainen, P.

doi:10.1007/s00158-007-0147-x

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…The discernibility matrix denoted by of the decision table means matrix (14) where and denote the values of the sample of attributes and , respectively, the and the line row element of the discernibility matrix , , and denotes that this case does not need to be considered. Hence it is interpreted as logic truth.…”

Section: ) Step 4 (Calculate the Discernibility Matrix)mentioning

confidence: 99%

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Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

Ding

Chai

Wang

et al. 2012

IEEE Trans. Ind. Inf.

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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.

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Section: ) Step 4 (Calculate the Discernibility Matrix)mentioning

confidence: 99%

“…Under the increased competitiveness of the world-wide market, the plant-wide optimization has attracted much attention in process industries [1]- [3], [12]- [14]. However, there are seldom reports on the plant-wide optimization for mineral processing [2].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

Ding

Chai

Wang

et al. 2012

IEEE Trans. Ind. Inf.

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“…In other words, if an optimisation update results in a local set-point which reduces the energy at the cost of increase in the process variations, that particular step of optimisation might be considered too risky to be applied. Nevertheless, an effective strategy might be considering the optimisation in the presence of uncertainty [14] . This way, both performance indices and the variations associated with them can be considered as optimisation objectives.…”

Section: Model-based Optimisationmentioning

confidence: 99%

Thermal Energy Reduction in Papermaking Industries

Brown

Afshar

Wang

et al. 2010

Measurement and Control

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“…The performance of global production optimization is usually evaluated by production indices [10]- [12]. The literature presents operational indices optimization as an important method for achieving global production optimization [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang

Ding

Jin

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Abstract-Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization 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 dataset 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 multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking 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 multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking 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 method to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multi-objective multi-tasking framework is developed to address the operational indices optimization, which includes a multi-taking multi-objective operational indices optimization problem formulation and a multi-taking 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 associate with a higher grade and yield of the product.

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Multiobjective process line optimization under uncertainty applied to papermaking

Cited by 13 publications

References 26 publications

Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

Thermal Energy Reduction in Papermaking Industries

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

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