Energy diagnosis and savings potential in knitted fabric based wet processing units

• Published in: Energy for sustainable development Vol. 54; pp. 85 - 100
• Main Authors: Kasturi, Srikant, Ayalur Kannappan, Bakthavatsalam
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.02.2020
• Subjects: Energy consumption; Green house gas (GHG) emission reduction; Knitted fabric; Resource conservation measures; Specific energy consumption (SEC); Textile wet processing industry; Textile wet processing industry; Energy consumption; Knitted fabric; Specific energy consumption (SEC); Green house gas (GHG) emission reduction; Resource conservation measures
• ISSN: 0973-0826
• DOI: 10.1016/j.esd.2019.10.003

Wet processing is the most energy intensive subsector in the textile industry. The complex nature of specific energy consumption (SEC) warrants a closer look at the independent parameters impacting the SEC. Energy audits were conducted in twelve knit fabric processing plants, processing fabric quality of 160–180 g per square meter, in South Asian countries to normalise independent parameters like fabric characteristics, geographic location and production processes. Energy consumption profiles, SEC, independent parameters impacting SEC, resource conservation measures (RCM) and their predicted impact on SEC were evaluated. Also the values for annual baseline Greenhouse Gas (GHG) emissions and predicted emission reduction were calculated. The most energy intensive fabric dyeing section consumed an average 32% of plant's electrical energy and 69% of the plant's steam. The baseline SEC of 39–84 GJ/t was predicted to be reduced to 29–58 GJ/t by implementing the RCMs. The baseline SECs had high correlation with values for plant capacity utilisation and heat rate for power generation. Priorities were established on the different areas for resource conservation for different plant configurations. The specific emission was predicted to reduce from 2.1-4.3 tCO2/t to 1.6–2.9 tCO2/t. The present work also contributes to the identification of replicable RCMs, which should be factored before benchmarking of SEC among similar plants. The effects of the recommended RCM methodology are identified and predicted but validation is not included. However, the predicted values for energy saving were found in line with previous literature. •Energy diagnosis of twelve knit fabric based wet processing plants were studied.•Fabric dyeing is the most energy intensive section - 69% of steam consumption.•Developed framework for quantifying replicable resource conservation measures (RCM).•Specific energy consumption (SEC) can be reduced by about 18%–40%.•Benchmarking plant performance should factor these replicable RCMs along with SEC.