review · Chemosphere
Textile and cosmetic manufacturing processes generate substantial volumes of dye-laden wastewater containing non-biodegradable chemicals, alongside high chemical and biological oxygen demand. Conventional biological, physical, and pressure-driven membrane systems often suffer from high costs, complexity, or low efficiency. Membrane distillation represents an alternative separation method capable of running on solar energy or low-grade waste heat. However, the technology faces operational hurdles, notably membrane fouling, temperature polarisation, and concentration polarisation. Most evaluated setups remain restricted to laboratory-scale testing. Advancing the technology requires tailored, high-porosity hydrophobic membranes alongside improved module configurations to curb energy losses while maximising permeate flux and dye rejection. Additionally, integration of artificial intelligence methodologies offers a route to streamline parameter fine-tuning, reduce experimental duration, and cut development costs, aiding the future progression of both synthetic and natural dye remediation.
Textile and cosmetic production produces heavily contaminated wastewater that is difficult and expensive to treat using conventional methods. Exploring low-energy treatment options, such as membrane distillation driven by waste heat or solar power, offers a path towards cleaner industrial discharge. Better understanding of membrane design and operational bottlenecks helps advance cleaner water purification practices for heavily polluting sectors.
The primary potential beneficiaries are textile and cosmetic manufacturers needing cost-effective dye effluent remediation. Operating systems using solar energy or waste heat could lower operational expenditure. However, the technology is at an early, laboratory-scale stage of development. Moving towards practical application will require custom-built high-porosity hydrophobic membranes, improved module designs to prevent fouling and energy loss, and algorithm-assisted system optimisation.
AI-generated from the published abstract. Always read the original work before citing.
Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization (CP and TP). Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, the nature of their time intensity poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.
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DOI: 10.1016/j.chemosphere.2024.142347
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