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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]

Long-term electricity consumption forecasting has been a hot issue due to its importance in power systems planning. Recently, system dynamics (SD) have been considered a potential method for electricity consumption forecasting. However, traditional SD methods are failed in forecasting future power systems toward low-carbon, due to the complex influencing factors and the simple SD model structure. In this study, a novel method combining SD and the ImPACT equation is proposed to solve the above problem. First, a new influencing factor system including emerging factors is established to consider the development of future power system. Subsequently, the ImPACT equation is employed to decompose the electricity consumption into four subsystems. By doing this, the linkage between factors and electricity consumption can be better revealed, which helps to improve the forecasting accuracy. Next, models for quantifying the correlation between subsystems and factors are established using SD. Finally, the proposed method is verified by forecasting the power consumption of China in the future year 2030 to 2050. Furthermore, various scenarios are set to analyze the changing trend of future power consumption by different influencing factors. The results of this article can provide important basic information for power supply and power grid planning. [ABSTRACT FROM AUTHOR]