Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method

Dynamic operation optimization has been utilized to realize optimal control problem for converter. The optimal control indicator is determined via current state of converter smelting production process, and the set values of operation variable would control converter production. Relationship between various operating variables, current temperature, and carbon content is constructed through operation analysis of a great deal of actual production data; then, the dynamic optimal control indicator is derived from historical excellent smelting data; finally, the dynamic operation optimization model is built by taking the minimum deviation between the current data—molten steel temperature and carbon content—and optimal data which are determined by the optimal control indicator as objective function. DE (differential evolution) with improved strategy is used to solve the proposed model for obtaining the set values of each operating variable, which is beneficial for further control. Simulation of actual production data shows the feasibility and efficiency of the proposed method. That proved that the proposed method solves the optimal control problem of converter steelmaking process as well

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

STATEMENT OF THE PROBLEM OPTIMAL CONTROL THE HARDNESS OF THE STEEL PRODUCED BASED ON THE MODEL OF TAKAGI-SUGENO-KANG

This study discusses the problem of mathematical modeling of complex technological systems under uncertainty to obtain the most optimal parameters in the management of the production process in the applied field – metallurgy. In the offered approach one of the most important tasks of management of technological process of steel smelting is considered: maintenance of the set hardness (calcification) of the steel distributed on depth of the smelted product. To minimize the inevitable errors associated with the expert choice of chemical composition, improve the management efficiency and the quality of the produced steel, it is proposed to apply the system of fuzzy production rules Takagi-Sugeno-Kanga (model TSK), based on the modeling of the dependence "composition-hardness". Application of this model will also allow to optimize the choice of the chemical composition of the steel in the conditions of stochasticity of the parameters of the regression models. In addition, in the study of the steel production process there is a need to solve the inverse problem – the determination of the chemical composition of the steel produced at a given hardness value. The proposed model of TSK based on fuzzy production rules for steel smelting prediction and control is presented in matrix form, so one of the possible ways to solve the control problem is to solve the corresponding matrix equation. At the same time, on the basis of experimental data, a significant shift in the estimates of the values of chemical elements was revealed. Therefore, governance must be based on an optimization approach. The proposed formulation of the optimization problem will develop an algorithm for solving the problem of optimal hardness control on the basis of the TSK model, characterized by the ability to automatically determine the required chemical composition of steel by a given distribution of its hardness. In addition, the developed model TSK using the optimal control problem will eliminate errors in determining the calculation model, as well as to determine the hardness of steel for the chemical composition does not fully correspond to a certain set of allowable intervals of changing the mass fractions of chemical elements

An efficient energy management in office using bio-inspired energy optimization algorithms

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme