Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

Abstract

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

Knowledge-data Collaborated Digital Twin Model of Papermaking Process

“…The emerging of digital twin in the industrial domain provides feasible solution ideas [24][25][26]. As aforementioned, although a large number of scholars have conducted modeling studies on digital twins and achieved some results in related fields [27][28][29], it remains empty of a robotic general digital twin frameworks for the chemical industry, especially for the papermaking industry. Therefore, this paper proposes a digital twin-based data visualization model based on CADSIM Plus simulation software for the papermaking industry to promote the process performance.…”

Prediction of parameters in the laser fading process of denim using convolutional neural networks

“…14 It was concluded that the neural network method can provide more accurate predictions than the linear regression method. Xu et al 15 used a combination of surrogate ensemble and particle swarm optimization to optimize the quantitative relationship between the denim bleaching performance of sodium hypochlorite bleached denim and its process variables. Similarly, they have also attempted to optimize the production cost of enzyme washing of indigo dyed cotton denim by combining kriging surrogate with a differential evolution algorithm.…”