Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing

Diana Goettsch, Krystel K. Castillo-Villar and Maria Aranguren
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Diana Goettsch: Department of Mechanical Engineering, Texas Sustainable Energy Research Institute, University of Texas at San Antonio, San Antonio, TX 78249, USA
Krystel K. Castillo-Villar: Department of Mechanical Engineering, Texas Sustainable Energy Research Institute, University of Texas at San Antonio, San Antonio, TX 78249, USA
Maria Aranguren: Department of Mechanical Engineering, Texas Sustainable Energy Research Institute, University of Texas at San Antonio, San Antonio, TX 78249, USA

Energies, 2020, vol. 13, issue 24, 1-18

Abstract: Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost.

Keywords: machine learning; neural networks; logistics; biomass; mathematical programming; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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