A Knowledge-reuse Based Intelligent Reasoning Model for Worsted Process Optimization

The textile process planning is a knowledge reuse process in nature, which depends on the expert’s knowledge and experience. It seems to be very difficult to build up an integral mathematical model to optimize hundreds of the processing parameters. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel knowledge-reuse based hybrid intelligent reasoning model (HIRM) for worsted process optimization. The model architecture and reasoning mechanism are respectively described. An applied case with HIRM is given to demonstrate that the best process decision can be made, and important processing parameters such as for raw material optimized.

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated by utilizing the scheduling system of the platform,and a minimum production time,i.e.,makespan decides whether the scheduling is optimal or not.This scheduling result allows manufacturers to achieve high productivity,energy savings,and customer satisfaction.Manufacturing in Industry 4.0 requires dynamic,uncertain,complex production environments,and customer-centered services.This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform.The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors.The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors:early delivery date and fulfillment of processing as many orders as possible.The genetic algorithm(GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem(JSSP)by comparing with the real-world data from a textile weaving factory in South Korea.The proposed platform will provide producers with an optimal production schedule,introduce new producers to buyers,and eventually foster relationships and mutual economic interests.

Removal of C.I.Reactive Red 2 by low pressure UV/chlorine advanced oxidation

Azo dyes are commonly found as pollutants in wastewater from the textile industry,and can cause environmental problems because of their color and toxicity.The removal of a typical azo dye named C.I.Reactive Red 2（RR2） during low pressure ultraviolet（UV）/chlorine oxidation was investigated in this study.UV irradiation at 254 nm and addition of free chlorine provided much higher removal rates of RR2 and color than UV irradiation or chlorination alone.Increasing the free chlorine dose enhanced the removal efficiency of RR2 and color by UV/chlorine oxidation.Experiments performed with nitrobenzene（NB）or benzoic acid（BA） as scavengers showed that radicals（especially OH） formed during UV/chlorine oxidation are important in the RR2 removal.Addition of HCO_3^- and Cl^- to the RR2 solution did not inhibit the removal of RR2 during UV/chlorine oxidation.