(307c) Targeting Maximum Energy and Water Efficiencies for the Sustainable Total Textile Waste Refinery

Authors: 
Kokossis, A. C., National Technical University of Athens
Nikolakopoulos, A., National Technical University of Athens
Barla, F., National Technical University of Athens
The global textile industry is among the top ten manufacturing economic activities attaining a market value of €0.82 trillion. In 2008, 24 million tonnes of cellulosic and 39 million tonnes of synthetic fibres were sold globally (Oerlikon report, 2009). Despite industry’s great economic value, the textile industry is among the most polluting ones. To only cultivate 1 kg of cotton; 16 g of pesticides, 460 g of fertilizer and 22 m3of water is consumed (Saleem et al., 2010). In EU alone, 10 million tonnes of textile waste are produced each year (EASME, 2015). Specifically, in central Europe, Nordic countries, USA and Japan, the total post-consumer textile waste going to landfill is estimated 0.6, 1.9, 0.6, 0.1, 0.4, 0.9 million tonnes respectively. The present work coordinates established and original Process Integration technologies to the solution of a new problem; the design of the sustainable Total Textile Waste Refinery (TTWR); a new biorefinery type, that it is being currently developed in the course of the European Horizon2020 research project (RESYNTEX, 2015). It uses the complete spectrum of waste textiles as feedstock, aiming to provide a holistic response to the largely overlooked waste textile management problem. TTWR is now just before the phase of pilot construction and process integration technologies are currently implemented to accelerate the design solution.

TTWR consists of gate links between textile materials with commodity and specialty products and chemicals. Feedstock includes the entire range of textile fibres, consisting mostly of cotton, polyethylene terephthalate (PET), polyamides (PAs), wool but also other material in smaller quantities like acrylics and elastane. Glucose, amino-acid solutions, and PET and PA monomers and oligomers are the gate chemicals produced by the decomposition of the feedstock before the process eventually produces bio-ethanol, adhesives used in the wood-based panel production, PET bottles and PA-derived value added chemicals (caprolactam, amylamine, hexanoic acid etc.). The decomposition of natural and synthetic textile fibres is realised using patented chemistries with emphasis on chemical and enzymatic hydrolysis. The overall process consists of 20 sections dedicated to pretreatment for the reduction of the feedstock size, discolorations for the removal of dyes and impurities, depolymerizations for the decomposition of material and the production of gate chemicals, separations and purification of gate chemicals, reactions and separations for the production of end products and finally solid and liquid waste treatment processes. At particular stages (discolorations, separations and purifications), the TTWR process can be considered as the reverse process of textile production (pretreatment, dying, polishing etc.) and therefore, it has similar characteristics regarding water consumption. Without any water integration attempts the water consumption is as high as 52 tonnes per tonne of feedstock, and it is shared among reaction and separation stages (mostly washing and filtration steps) at a 2:1 ratio. Contrary to the textile fabrication processes which uses an average of 30 MJ/kg of fabric (Kalliala and Nousianinen, 1999), TTWR is more energy intensive, requiring more than 53 MJ/kg of feedstock. The majority of the energy requirements (~40%) is used in reactions, which are mainly neutral thermochemical hydrolyses at high temperatures for PAs and PET, and biochemical hydrolyses at mild temperatures. Considering that water is used at high ratios compared to the feedstock material in all reactions, it is also the main energy carrier in the reactors. This evidence indicates clear scope for a combined energy and water integration study, since water is used both as a cleaning agent for removing dyes and impurities as well as the main heating medium.

The TTWR process design procedure benefits from the rich technical know-how that has been accumulated so far for the design of conventional chemical processes. The present work takes advantage and improves on the significant contributions in process synthesis and integration (Mountraki et al., 2011a, 2011b; Tsakalova et al., 2012) made in the course of the European research project BIOCORE (Biocore, 2014) dedicated to the evolution of 2ndgeneration biorefineries.

Novel process integration tools have been developed for assessing alternative process integration scenarios and achieving maximum efficiencies in energy and water use in modern biorefineries (Koufolioulios et al., 2014; Nikolakopoulos and Kokossis, 2016). Also, total waste management and recycle design technology has been produced to enable the development of designs for processes with minimum environmental impact (Mountraki et al., 2016; Nikolakopoulos et al., 2016a).

Water and energy Integration is applied with a combined use of process modelling targeting, process synthesis and mathematical optimization. Targeting sets the scope to match and improve efficiencies for energy and water (Pinch, Water Pinch), explaining efficiency bottlenecks, modifications required to improve targets, means to re-use and regenerate water and solvents, as well as means and technologies appropriate to treat waste.

A new tool is proposed In this paper for simultaneously targeting maximum energy and water efficiencies in the TTWR. It is developed by integrating a mathematical programming heat cascade sub-model for targeting and optimally allocating heating and cooling utilities (Koufolioulios et al., 2014) with a two-step iterative procedure to coordinate the targeting of minimum fresh water requirements, the calculation of recycle and treatment flows and the selection of water treatment processes (Nikolakopoulos et al., 2016b).

For the first model, combinations of utilities are built by matching pseudo-cooling utilities with steam levels so as to permit alternation of the purpose of the utility from cooling to heating and thus exploit excess of heat. This actually defines a dual function for a subset of cold utilities. The method calculates for each combination of utilities the minimum utility requirements through an expanded transhipment model of (Papoulias and Grossmann, 1983). The model incorporates the utilities as process streams with the objective of minimizing total utility cost. Heat receivers (i.e. cool streams matched against steam at the same level) are being maximized by being assigned a negative sign. Hot and cold utilities at the same level are assigned different costs and prices respectively. The model calculates the utility combination that minimizes the annualized total cost and maximizes profit.

The second model, consists of two interlinked transhipment models, where successively increasing recycle flows are introduced until convergence to the optimal recycle flow value based on a total cost criterion. The first is a transhipment model used to identify the flowrates and concentrations of the wastewater streams at the effluent water mains. The second model, originally intended for targeting treatment flowrates (Nikolakopoulos et al., 2014) is extended in screening treatment technologies and suggesting processes to integrate. The models demonstrated cascade characteristics by allowing the transfer of contaminant between concentrations intervals.

The application of the proposed tool in the design of the TTWR is able to produce significant energy and water saving targets, that may reach up to 50% for energy and 70% for water. An additional advantage of the procedure is the possibility to detect critical parameters of the system, where retrofitting changes improve on total cost. These are the number and order of washing and filtering stages and their concurrent or countercurrent mode of operation. The results of total water network suggest the use of centralized treatment using ultimately ultra and nano-filtration preceded by simple pretreatment filtering stages.

Acknowledgement

 This project has received funding from the European Union’s Horizon 2020 research and innovation programunder grant agreement No 641942.

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