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

Jie Xu,Feng Liu,Sheng Li,Zhenglei He,Zongao Zhang

doi:10.1177/15589250211022331

Jie Xu, Feng Liu + Show 3 more

Open Access

https://doi.org/10.1177/15589250211022331

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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.

Highlights

Denim garment has gained popularity for a long time, its various styles are mainly created by unique washing techniques in the manufacturing process
The details of the framework are explained as follow: Section 3.1 describes the background of the ingredients of the ensemble of surrogates (ESs), including radial basis function neural network (RBFNN), support vector regression (SVR) and random forest (RF); Section
For the contrast of RSM, the ESs performed better in most situations, while the RSM had a quite good performance in tensile strength in weft direction. This phenomenon just indicates that choosing the most appropriate surrogate model for an unexplored engineering problem is a thorny question, because we cannot know which surrogate will perform best based on the specific problem property and existing training samples

Summary

Introduction

Denim garment has gained popularity for a long time, its various styles are mainly created by unique washing techniques in the manufacturing process. By Kan.[4] Among various washing techniques, the sodium hypochlorite bleaching washing, named as chlorine bleaching, has been applied for a long time, and it still plays a dominant part in industrial production because of fine performances. Few previous works constructed a quantitative mapping model between input variables and washing effects, which results in that the trial-and-error method is still extensively conducted in the chlorine bleaching process for denim garment production, and may bring about a great waste of resources in some extent. The proposed hybrid framework of ESs-PSO is used to purse the optimal cost of chlorine bleaching production. 3.2 introduces how to integrate the stand-alone surrogates together; Section 3.3 presents an introduction of PSO; Section 3.4 gives the description of how the proposed ESsPSO works

Methods

Results

Conclusion