Model and Algorithm for Planning Hot-Rolled Batch Processing under Time-of-Use Electricity Pricing

Abstract

Batch-type hot rolling planning highly affects electricity costs in a steel plant, but previous research models seldom considered time-of-use (TOU) electricity pricing. Based on an analysis of the hot-rolling process and TOU electricity pricing, a batch-processing plan optimization model for hot rolling was established, using an objective function with the goal of minimizing the total penalty incurred by the differences in width, thickness, and hardness among adjacent slabs, as well as the electricity cost of the rolling process. A method was provided to solve the model through improved genetic algorithm. An analysis of the batch processing of the hot rolling of 240 slabs of different sizes at a steel plant proved the effectiveness of the proposed model. Compared to the man–machine interaction model and the model in which TOU electricity pricing was not considered, the batch-processing model that included TOU electricity pricing produced significantly better results with respect to both product quality and power consumption.

An Improved Elitist GA-based Solution for Integrated Batch Planning Problem in a Steelmaking Plant

“…Cui et al 20) proposed a difference of convex functions algorithm to solve the scheduling problem of steelmaking -continuous casting process. At present, the most widely used method is intelligent solving method, which uses intelligent optimization algorithm to solve the production planning and scheduling problems, for example, tabu search algorithm, 21) genetic algorithm 22,23) or hybrid algorithm 24,25) combining multiple algorithms. Lin et al 26) used a modified imprecision-propagating multi-objective evolutionary algorithm to solve the multi-objective integrated model of continuous cast-hot rolling section.…”

“…The algorithm should construct a two-dimensional matrix to represent the structure of the solution in the chromosome decoding process. Equation (25) shows the corresponding relationship between the gene b i on the chromosome and the position in the two-dimensional matrix. In the decoding process, the two-dimensional matrix shall follow the following conditions:…”

Special Issue on “Process Modeling in Pyrometallurgical Engineering”

“…Hu et al [38] developed an optimization model for hot rolling based on the time-of-use (TOU) electricity pricing using a genetic algorithm. Jumps between adjacent slabs in width, hardness, and thickness were avoided by including penalties in the objective function.…”

An Improved NSGA-II for Energy-oriented Hot Rolling Production Scheduling Problems

“…In many actual power-intensive industrial production, the electric cost is an important part of energy cost [8]. Hu et al [9]considered the goal of minimizing the total technical penalty and the electricity cost of the rolling process, and used the improved genetic algorithm to solve this problem. Liu et al [10] combined particle swarm optimization (PSO) and simulated annealing (SA) to solve the hot strip mill scheduling problem which is formulated as a multi-objective optimization problem with multi-constraints.…”