Mixed-Framework-Based Energy Optimization of Chemi-Mechanical Pulping

Abstract

The papermaking industry supplies the carrier for literature and packaging via chemi-mechanical pulping, which is the most commonly used approach for converting wood chips into cellulose fibers. In 2016, Chinese papermaking enterprises consumed 41.05 million tons of standard coal. Thus, it is important to reduce the electrical energy consumed in mechanical pulp production by optimizing the operating conditions of primary stage refiners. This article reports a two-stage optimization algorithm to achieve this objective. First, mixed data sampling regression combined with a new weighting scheme derived from mixed flow conditions is used to encode the characteristics of high consistency refiner (HCR) in the manufacturing system. Then, the genetic algorithm with self-adaptive population searches for the optimal operating parameters that can minimize the power cost of the papermaking process. The proposed mixed framework and its separate components are validated through numerical functions and practical datasets, all of which have competitive performances compared to other algorithms. After being applied to a papermaking plant for two months, the intelligent optimization algorithm is found to have significant economic achievements in reducing the HCR energy consumption by 1.67 kWh/adt on an average scale.