(436e) Development of an Interactive Software Tool for Designing Industrial Solvent Recovery Processes | AIChE

(436e) Development of an Interactive Software Tool for Designing Industrial Solvent Recovery Processes


Stengel, J. - Presenter, Rowan University
Chea, J., Rowan University
Aboagye, E., Rowan University
Mackley, M., Rowan University
Geier, J., Rowan University
Yenkie, K., Rowan University

In 2017 the chemical industry was reported to be the world’s second-largest manufacturing sector (United Nations Environment Programme 2019). Solvents are used in most operations in chemical and pharmaceutical industries as a reaction medium, selective dissolution and extraction medium, and dilution agents. Thus, there is a sizable amount of solvent waste generated due to process inefficiencies such as inefficient mixing, and inappropriate process equipment being utilized. There are several options for solvent waste disposal, however, the three most common are on-site disposal, off-site disposal, and incineration (J. Raymond, Stewart Slater, and J. Savelski 2010). Even though these methods of solvent waste disposal carry considerable negative environmental impacts they are still widely used since they are readily available and provide an easy method to dispose of large quantities of waste. Solvent recovery is typically not used because of the potential difficulties in achieving a required purity to meet the stringent guidelines imposed on the products in pharmaceuticals and specialty chemical industries as well as the additional infrastructure and investments needed. To this end, this problem needs to be studied carefully by involving all aspects in regard to capital needs, environmental benefits, comparison to traditional disposal methods, while achieving the required chemical purity.

Thus, designing a solvent recovery method is a multi-objective problem focused on minimizing capital investments of the new separation technologies while maximizing the amount of solvent recovery matching the desired purity requirements. This task is further complicated because there is a lack of a guidance system to assist professional engineers in the modeling and design of solvent recovery processes. Such a guidance system or tool that could predict the value of solvent recovery and reuse for industry users, can generate an environmentally friendly trend to shift away from using incineration, on-site disposal, and off-site disposal as primary solvent waste handling methods. As seen in Figure 1A, not only will this tool provide a greener alternative, but also aid in solvent recycling into the chemical plant, which is economically favorable. To this end, if we can develop a user-friendly tool that allows engineers to easily access solvent recovery options, we can provide an economical and environmentally favorable strategy.

To account for all possible waste streams a robust framework is needed. This consists of a maximal process flow diagram that encompassed multiple stages of separations and multiple technologies within those stages. Together this process flow diagram develops the superstructure that provides multiple technology pathway options for any solvent waste stream. We established the different stages in the superstructure as Solid Removal, Recovery, Purification, and Refinement by reviewing the literature on existing materials recovery and separation processes (Yenkie, Wu, and Maravelias 2017; Biegler 1997; Geankoplis 2003). Common technologies are placed in different stages depending on the component they can separate in terms of their physical and chemical properties. A comprehensive chemical database is created using sources, such as the Design Institute for Physical Properties (DIPPR), to store all relevant chemical and physical properties (“DIPPR” 2012). Using General Algebraic Modeling Systems (GAMS) to model the various separation units and stream flows as a multi-objective optimization problem, the amount of solvent recovered can be optimized while minimizing the environmental impacts and total cost of operation (Chea et al. 2020; Yenkie et al. 2016). With the GAMS code as the backbone, a Graphical User Interface (GUI) is created to provide a user-friendly tool to the chemical industry. Figure 1B shows how the GUI connects the superstructure modeled in GAMS model, the chemical database, and user inputs to allow for anyone in the industry to find an optimal technology pathway for their solvent waste.

The GUI will allow for a user to input chemical species and parameters that are specific to the process that is being observed. By connecting MATLAB to the GAMS code, it is possible to model a solvent recovery process solely in the GUI with these input parameters. This tool reports economic, chemical, and environmental factors for the user to consider. The economic factors include an annualized cost breakdown, utility costs, consumable costs, labor costs, and overhead costs. These costs can be seen for the whole selected recovery pathway with details of the individual technologies. The chemical factors report the amount of solvent the predicted pathway can recover and its purity level. The environmental factors show the estimated emissions from the technology pathway and compare these emissions against those for incinerating the waste stream. Thus, this tool is a powerful option for those in the industry who want to look for a cost-effective way of recycling their solvents from the waste streams.

Keywords: Solvent Recovery, Recycling, Graphical User Interface


This research was supported by the United States Environmental Protection Agency’s Pollution Prevention (P2) Program (NP96259218). The authors thank the Department of Chemical Engineering at Rowan University for their continued assistance with acquiring the tools required for this research.


Biegler, Lorenz. 1997. Systematic Methods of Chemical Process Design. 1st ed. Prentice Hall PTR.

Chea, John D., Austin L. Lehr, Jake P. Stengel, Mariano J. Savelski, C. Stewart Slater, and Kirti M. Yenkie. 2020. “Evaluation of Solvent Recovery Options for Economic Feasibility through a Superstructure-Based Optimization Framework.” Industrial & Engineering Chemistry Research 59 (13): 5931–44. https://doi.org/10.1021/acs.iecr.9b06725.

“DIPPR.” 2012. The American Institute of Chemical Engineers. March 29, 2012. aiche.org/dippr.

Geankoplis, Christie J. 2003. Transport Processes and Unit Operations. 4th ed. Englewood Cliffs, NJ: P T R Prentice-Hall, Inc.

  1. Raymond, Michael, C. Stewart Slater, and Mariano J. Savelski. 2010. “LCA Approach to the Analysis of Solvent Waste Issues in the Pharmaceutical Industry.” Green Chemistry 12 (10): 1826–34. https://doi.org/10.1039/C003666H.

United Nations Environment Programme. 2019. “Global Chemicals Outlook II - From Legacies to Innovative Solutions - Synthesis Report (2019).” Global Chemicals Outlook II. United Nations Environment Programme. https://wedocs.unep.org/bitstream/handle/20.500.11822/27651/GCOII_synth.....

US EPA, OEI. 2013. “TRI Data and Tools.” Data and Tools. US EPA. March 3, 2013. https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-and-tools.

Yenkie, Kirti M., WenZhao Wu, Ryan L. Clark, Brian F. Pfleger, Thatcher W. Root, and Christos T. Maravelias. 2016. “A Roadmap for the Synthesis of Separation Networks for the Recovery of Bio-Based Chemicals: Matching Biological and Process Feasibility.” Biotechnology Advances 34 (8): 1362–83. https://doi.org/10.1016/j.biotechadv.2016.10.003.

Yenkie, Kirti M., Wenzhao Wu, and Christos T. Maravelias. 2017. “Synthesis and Analysis of Separation Networks for the Recovery of Intracellular Chemicals Generated from Microbial-Based Conversions.” Biotechnology for Biofuels 10 (1). https://doi.org/10.1186/s13068-017-0804-2.