(198g) Emergy and SPI Assessment of Solvent Recovery Pathways

Introduction

Conventional waste solvent handling techniques such as incineration, onsite, and offsite disposal increase the process's ecological burden and carbon footprint (Cseri et al., 2018; Das et al., 2012; Oppelt, 1990; Riber, 2007). The solvent waste generation will continue to increase as global solvent consumption expands (Chea et al., 2020). Sustainability assessment has become fundamentally important to decision-makers due to the increased environmental concerns associated with waste solvent disposal. Therefore, the recovery and reuse of solvents from waste streams is paramount to improving the overall sustainability of industrial processes (Cavanagh et al., 2014; Slater et al., 2010, 2007).

Emergy and Sustainable Process Index (SPI) are sustainability metrics that have can be used to evaluate the energy efficiency and greenness of industrial processes (Brown and Ulgiati, 2002; Krotscheck and Narodoslawsky, 1996; Narodoslawsky, 2015; Ulgiati et al., 1994). Emergy, defined as the amount of available energy of one kind that is directly and indirectly used up in transformations to make a product, can help identify the major energy-based hotspots within a process. Emergy helps to account for all the work done by nature and human-economy using solar-emjoules (sej) as the basis for calculation. However, SPI is an ecological footprint that evaluates the environmental burden of processes. It estimates the total area needed by a process to produce a unit product or service. In sustainability assessment, it is important to have multiple indices, as a single metric is insufficient in capturing and addressing the various dimensions encompassing a process. Therefore, to offer a holistic sustainability analysis for solvent recovery processes, both SPI and Emergy, need to be integrated in the evaluations.

Methodology

In this work, the Emergy and SPI of solvent recovery framework has been evaluated using a superstructure-based optimization approach that captures all the complexities of the problem. Individual technologies were categorized into stages. The Emergy models include contents of the waste solvent, material of construction of technologies, labor, purchase cost of technologies, utilities, maintenance and overhead costs. The SPI models consist of areas needed for raw material consumption, energy consumption, infrastructure, staff accommodation, embedded air, water, and soil emissions into the ecosphere. The optimization was formulated as a mixed-integer non-linear programming (MINLP) problem. The objective was to simultaneously minimize the SPI, Emergy and Economics of the recovery pathway while meeting the exit stream’s purity requirements.

Results & Discussion

The recovery framework was applied to an illustrative case study. In this case study the goal is to recover dimethoxyethane (DME) at a purity of 95% from a process waste stream consisting of 21.3% DME, 35.3% water, 41.3% toluene, and 1.3% 1-ethoxy-1-methoxy ethane (EME). The combined capital and operating cost of the recovery process is $330,000 per annum (Chea et al., 2020). Figure 1 shows the results for the SPI and Emergy analysis for this representative case study. It can be observed that the major contribution to Emergy is the maintenance and overhead cost while the area needed to accommodate water emissions is the dominant footprint for the SPI analysis. The results support the need to have multiple metrics to help identify hotspots and implement corrective measures to reduce the impacts of the major contributing footprints. This was observed based on the representative case study

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