Multi-objective biofuel feedstock optimization considering different land-cover scenarios and watershed impacts

Ana Cram; Jose Espiritu; Heidi Taboada; Delia J. Valles-Rosales; Young Ho Park; Efren Delgado; Jianzhong Su; Ana Cram; Jose Espiritu; Heidi Taboada; Delia J. Valles-Rosales; Young Ho Park; Efren Delgado; Jianzhong Su

doi:10.3934/ctr.2022006

Clean Technologies and Recycling

2022, Volume 2, Issue 2: 103-118. doi: 10.3934/ctr.2022006

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Research article

Multi-objective biofuel feedstock optimization considering different land-cover scenarios and watershed impacts

1.
Department of Industrial, Manufacturing, and Systems Engineering, University of Texas at El Paso, El Paso, TX 79968, USA
2.
Department of Mechanical and Industrial Engineering, Texas A & M University–Kingsville, Kingsville, TX 78363, USA
3.
Department of Industrial Engineering, New Mexico State University, Las Cruces, NM 88003, USA
4.
Department of Mechanical & Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA
5.
Department of Family and Consumer Sciences, Food Science and Technology, New Mexico State University, Las Cruces, NM 88003, USA
6.
Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019, USA

Received: 13 January 2022 Revised: 18 May 2022 Accepted: 23 May 2022 Published: 07 June 2022

This research presents a novel optimization modeling framework for the existing Soil and Water Assessment Tool (SWAT), which can be used to optimize perennial feedstock production. This novel multi-objective evolutionary algorithm (MOEA) uses SWAT outputs to determine optimal spatial placement of variant cropping systems, considering environmental impacts from land-cover change and management practices. The final solution to the multi-objective problem is presented as a set of Pareto optimal solutions, where one is suggested considering the proximity to the ideal vector [1,0,0,0]. This unique approach provides a well-suited method to assist researchers and stakeholders in understanding the environmental impacts when cultivating biofuel feedstocks. The application of the proposed MOEA is illustrated by analyzing SWAT's example data set for Lake Fork Watershed. Nine land-cover scenarios were evaluated in SWAT to determine their optimal spatial placement considering maximizing biomass production while minimizing sediment yield, organic nitrogen yield, and organic phosphorous yield.
- multi-objective optimization,
- Soil and Water Assessment Tool,
- watershed management,
- land-cover,
- biofuel feedstock,
- sediment yield
Citation: Ana Cram, Jose Espiritu, Heidi Taboada, Delia J. Valles-Rosales, Young Ho Park, Efren Delgado, Jianzhong Su. Multi-objective biofuel feedstock optimization considering different land-cover scenarios and watershed impacts[J]. Clean Technologies and Recycling, 2022, 2(2): 103-118. doi: 10.3934/ctr.2022006

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Abstract

This research presents a novel optimization modeling framework for the existing Soil and Water Assessment Tool (SWAT), which can be used to optimize perennial feedstock production. This novel multi-objective evolutionary algorithm (MOEA) uses SWAT outputs to determine optimal spatial placement of variant cropping systems, considering environmental impacts from land-cover change and management practices. The final solution to the multi-objective problem is presented as a set of Pareto optimal solutions, where one is suggested considering the proximity to the ideal vector [1,0,0,0]. This unique approach provides a well-suited method to assist researchers and stakeholders in understanding the environmental impacts when cultivating biofuel feedstocks. The application of the proposed MOEA is illustrated by analyzing SWAT's example data set for Lake Fork Watershed. Nine land-cover scenarios were evaluated in SWAT to determine their optimal spatial placement considering maximizing biomass production while minimizing sediment yield, organic nitrogen yield, and organic phosphorous yield.

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