The expenses of pig iron manufacturing stay in a relatively high level due to the large consumption of coke and ore. There are a variety of methods to optimize the ironmaking process, including multi-indexes method, production-oriented approach, and cost control strategy. In order to reduce the production cost, we optimize the raw materials supply schedule of an ironmaking plant with five sintering machines and seven blast furnaces. The ironmaking plant used to fix the feeding system, leading to an inefficient operation and mismatched connection between these two procedures. At first, the physical characteristics of each blast furnace is captured by linear regression model. Then, a self-adaptive genetic algorithm with variable population size is constructed under distribution system. Finally, the improved genetic algorithm is applied to optimize the total production cost represented by the aggregation of seven coke ratios on a cloud computing platform. With the application of this cloud collaborative optimization framework, the mean coke ratio of the ironmaking factory has decreased 13.96kg/t Fe.

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.