| Autor: |
Lujan Garcia-Castro, Florencia, Ruiz-Femenia, Rubén, Salcedo Díaz, Raquel, Caballero, José A. |
| Přispěvatelé: |
Universidad de Alicante. Departamento de Ingeniería Química, Universidad de Alicante. Instituto Universitario de Ingeniería de los Procesos Químicos, Computer Optimization of Chemical Engineering Processes and Technologies (CONCEPT) |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
Nowadays there is a growing concern regarding greenhouse gas emissions and their consideration in the supply chain design. In this work we present a robust stochastic model for the design of a supply chain under uncertainty of CO2-allowance prices and market demand. The three-echelon petrochemical supply chain superstructure consists of four production plants in Europe, storage associated with these plants and four possible markets. At each plant different products can be produced according to the available technologies. The goal is to maximize the expected net present value (ENPV), while reducing the amount of CO2 equivalent emissions. We implemented the carbon cap and trade model from the European Union emissions Trading System, whose goal is to reduce the emission cap over time in order to achieve a climate-neutral EU by 2050. We combine the environmental LCIA data, required to determine the global warming potential, with the forecast of CO2 allowance prices. The problem involves a multi period mixed-integer linear program (MILP) formulation, which was implemented in Python using Pyomo and solved using IBMs CPLEX algorithm. To deal with uncertainty in market demand and CO2-allowance prices, we implemented an ARIMA model and generated multiple scenarios. Since a full discretization of the resulting probability space leads to a number of scenarios that exceeds capacities of state-ofthe-art computers with ease, decomposition techniques are applied. The obtained results show an improvement of the economic performance when compared to the results from the deterministic approach that is being widely used in literature. The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain under project PROMETEO/2020/064. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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