Pub Date : 2018-01-01Epub Date: 2018-07-04DOI: 10.1016/B978-0-444-64235-6.50071-1
Styliani Avraamidou, Aaron Milhorn, Owais Sarwar, Efstratios N Pistikopoulos
While the importance of the Food-Energy-Water Nexus (FEW-N) has been widely accepted, a holistic approach to facilitate decision making in FEW-N systems, along with a quantitative index assessing the integrated FEW-N performance is rather lacking. In this work, we propose a FEW-N metric along with a framework to facilitate decision making for FEW-N process systems through a FEW-N integrated approach. The framework and metric are illustrated through a case study on a dairy production and processing plant. The dairy industry is a significant user of water and energy, with water being a top issue for most dairy industries and organizations worldwide. Following the framework, we develop a mixed-integer scheduling model, with alternative pathways, that faithfully replicated the major food, energy, and water aspects of a real cottage-cheese production plant. Using the developed FEW-N metric we were able to optimize the cottage-cheese plant process and observe different trade-offs between the FEW-N elements.
{"title":"Towards a Quantitative Food-Energy-Water Nexus Metric to Facilitate Decision Making in Process Systems: A Case Study on a Dairy Production Plant.","authors":"Styliani Avraamidou, Aaron Milhorn, Owais Sarwar, Efstratios N Pistikopoulos","doi":"10.1016/B978-0-444-64235-6.50071-1","DOIUrl":"https://doi.org/10.1016/B978-0-444-64235-6.50071-1","url":null,"abstract":"<p><p>While the importance of the Food-Energy-Water Nexus (FEW-N) has been widely accepted, a holistic approach to facilitate decision making in FEW-N systems, along with a quantitative index assessing the integrated FEW-N performance is rather lacking. In this work, we propose a FEW-N metric along with a framework to facilitate decision making for FEW-N process systems through a FEW-N integrated approach. The framework and metric are illustrated through a case study on a dairy production and processing plant. The dairy industry is a significant user of water and energy, with water being a top issue for most dairy industries and organizations worldwide. Following the framework, we develop a mixed-integer scheduling model, with alternative pathways, that faithfully replicated the major food, energy, and water aspects of a real cottage-cheese production plant. Using the developed FEW-N metric we were able to optimize the cottage-cheese plant process and observe different trade-offs between the FEW-N elements.</p>","PeriodicalId":72950,"journal":{"name":"ESCAPE. European Symposium on Computer Aided Process Engineering","volume":"43 ","pages":"391-396"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/B978-0-444-64235-6.50071-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36585743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01Epub Date: 2018-07-04DOI: 10.1016/B978-0-444-64235-6.50076-0
Melis Onel, Burcu Beykal, Meichen Wang, Fabian A Grimm, Lan Zhou, Fred A Wright, Timothy D Phillips, Ivan Rusyn, Efstratios N Pistikopoulos
The ultimate goal of the Texas A&M Superfund program is to develop comprehensive tools and models for addressing exposure to chemical mixtures during environmental emergency-related contamination events. With that goal, we aim to design a framework for optimal grouping of chemical mixtures based on their chemical characteristics and bioactivity properties, and facilitate comparative assessment of their human health impacts through read-across. The optimal clustering of the chemical mixtures guides the selection of sorption material in such a way that the adverse health effects of each group are mitigated. Here, we perform (i) hierarchical clustering of complex substances using chemical and biological data, and (ii) predictive modeling of the sorption activity of broad-acting materials via regression techniques. Dimensionality reduction techniques are also incorporated to further improve the results. We adopt several recent examples of chemical substances of Unknown or Variable composition Complex reaction products and Biological materials (UVCB) as benchmark complex substances, where the grouping of them is optimized by maximizing the Fowlkes-Mallows (FM) index. The effect of clustering method and different visualization techniques are shown to influence the communication of the groupings for read-across.
{"title":"Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques.","authors":"Melis Onel, Burcu Beykal, Meichen Wang, Fabian A Grimm, Lan Zhou, Fred A Wright, Timothy D Phillips, Ivan Rusyn, Efstratios N Pistikopoulos","doi":"10.1016/B978-0-444-64235-6.50076-0","DOIUrl":"10.1016/B978-0-444-64235-6.50076-0","url":null,"abstract":"<p><p>The ultimate goal of the Texas A&M Superfund program is to develop comprehensive tools and models for addressing exposure to chemical mixtures during environmental emergency-related contamination events. With that goal, we aim to design a framework for optimal grouping of chemical mixtures based on their chemical characteristics and bioactivity properties, and facilitate comparative assessment of their human health impacts through read-across. The optimal clustering of the chemical mixtures guides the selection of sorption material in such a way that the adverse health effects of each group are mitigated. Here, we perform (i) hierarchical clustering of complex substances using chemical and biological data, and (ii) predictive modeling of the sorption activity of broad-acting materials via regression techniques. Dimensionality reduction techniques are also incorporated to further improve the results. We adopt several recent examples of chemical substances of Unknown or Variable composition Complex reaction products and Biological materials (UVCB) as benchmark complex substances, where the grouping of them is optimized by maximizing the Fowlkes-Mallows (FM) index. The effect of clustering method and different visualization techniques are shown to influence the communication of the groupings for read-across.</p>","PeriodicalId":72950,"journal":{"name":"ESCAPE. European Symposium on Computer Aided Process Engineering","volume":"43 ","pages":"421-426"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284807/pdf/nihms-989329.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36767012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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