Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains

Aboytes–Ojeda, Mario; Castillo-Villar, Krystel K.; Ekşioğlu, Sandra D.

doi:10.1007/s10479-019-03477-8

Cited by 23 publications

(15 citation statements)

References 26 publications

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“…Therefore, a higher inventory level cannot guarantee a good service level in the supply chain system. The assumptions therein are not tenable similarly, for the lower half of the conflict graph; it is assumed that if the overall inventory of the supply chain keeps a low inventory level, various risks caused by inventory can be reduced in the supply chain system [ 20 ]. Especially in the supply chain of fashion products, the demand volatility is often very large, especially in some seasonal peak demand; too low inventory level will make the supply chain system unable to meet the market demand, and then, have to face the risk of more severe market competition.…”

Section: Methodsmentioning

confidence: 99%

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Optimization Design of Short Life Cycle Product Logistics Supply Chain Scheme Based on Support Vector Machine

2022

Computational Intelligence and Neuroscience

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In order to better solve the problem of product logistics supply chains with short life cycles, a solution optimization of short life cycle product logistics supply chains based on support vector machines is proposed. This method recommends key technical problems and solutions through information represented by support vector machines and explore the research of short life cycle products to realize logistics supply chains. The research shows that, whether it is a retail channel or a network channel, the RMSE value of the effect index predicted by SVM is smaller than the RMSE value of the improved Bass. It can be seen that the SVM demand forecasting model constructed by considering multiple input factors can obtain a more accurate forecasting effect. The accuracy of the demand forecasting model based on SVM is verified.

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Section: Methodsmentioning

confidence: 99%

“…Parameters. After the regression decision function is obtained, the normalized forecast sample is substituted into the regression decision equation, that is, the value y of the demand forecast between (0, 1) can be obtained, which can be converted into the actual forecast value by using the formula (20):…”

Section: Determining Svm Optimizationmentioning

confidence: 99%

Optimization Design of Short Life Cycle Product Logistics Supply Chain Scheme Based on Support Vector Machine

2022

Computational Intelligence and Neuroscience

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“…Second, the densification and drying that takes place in depots and their proximity to suppliers and mass transportation railways has the potential to reduce total SC transportation costs. Aboytes-Ojeda et al, 2018 [19] introduce a two-stage stochastic model that uses a hub-and-spoke network, including depots that preprocess biomass. In addition to moisture variability, the model also considers the natural variability in biomass ash content when determining the depots' locations, biorefineries locations, selection of conversion technologies, and the biomass required for bioethanol production.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bowling, and El-Halwagi 2011 [17] Castillo-Villar, Eksioglu, Taherkhorsandi, 2017 [18] Aboytes-Ojeda, Castillo-Villar Eksioglu, 2018 [19] This Work Hub-and-spoke network X X X X Considers variability in X X X moisture and ash content Utilizes depots to reduce X X transportation costs and processing variability Uses L-shape Method X X X Considers multiple biomass X byproducts…”

Section: Authorsmentioning

confidence: 99%

Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains

Keith

Castillo-Villar

2023

Energies

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Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byproducts: bio-oil (60–75%), biochar (15–25%), and syngas (10–20%), depending on the feedstock and its composition. The energy required to convert the biomass varies depending on the levels of cellulose, hemicellulose, and lignin. This work proposes a novel two-stage stochastic model that designs an efficient biomass supply chain mindful of the trade-offs between pyrolysis byproducts (bioethanol and biochar). Remarkably, the model integrates biomass quality-related costs associated with moisture and ash content such as the energy consumption of preprocessing equipment and boiler maintenance due to excess ash. Biomass quality directly affects the production yield as well as the total cost of production and distribution. The results from our case study indicate a shortage of biomass from the suppliers to fulfill the demand for biochar from the power plants and bioethanol from the cities. Furthermore, the bioethanol price has the most impact on the total supply chain according to our sensitivity analysis.

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“…This is seldom done in literature, as the majority of published works only consider the products when they are ready to be harvested. In another example, Castillo-Villar developed a two stage linear stochastic programming model to minimize costs related to transportation, location, technology and quality with a case study in the state of Tennessee (Aboytes-Ojeda et al, 2019). The stochastic parameters included ash and moisture contents.…”

Section: Literature Reviewmentioning

confidence: 99%

Marine Biofuels Costs and Emissions Study for the European Supply Chain Till 2030

Gartland

Pruyn

2022

Front. Energy Res.

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The design and preliminary estimations of biomass supply chains are essential in matching energy supply to energy demand. This is especially true of novel/future fuels and technologies in large industries. In this paper, a Mixed Integer Linear Programming (MILP) model was formulated to represent biofuel supply chains across Europe for the production of three novel marine fuels and to allow the selection of fuel conversion technologies, biomass supply locations, and the logistics of transportation from resources to conversion and from conversion to final markets. On top of this, the total production costs and emissions were calculated and compared to current marine fuels to assess the implementation potential and feasibility of these fuels. The MILP model was used to design and analyze optimal distribution and conversion systems, using a realistic data-set covering the European member states and 15 of the largest bunkering ports in the EU. The results showed that on average, the fuels obtained a 72% greenhouse gas (GHG) reduction compared to a fossil fuel comparator and ranged from 22–36 €/GJ in total production costs. It was also discovered that forestry residues were the best-suited biomass for the production of these fuels and that Poland had the highest supply potential of all considered states. The available supply of biomass was sufficient for the demand in the foreseeable future, the largest impediment to the adoption of these fuels is the available refining potential in Europe.

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Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains

Cited by 23 publications

References 26 publications

Optimization Design of Short Life Cycle Product Logistics Supply Chain Scheme Based on Support Vector Machine

Optimization Design of Short Life Cycle Product Logistics Supply Chain Scheme Based on Support Vector Machine

Stochastic Programming Model Integrating Pyrolysis Byproducts in the Design of Bioenergy Supply Chains

Marine Biofuels Costs and Emissions Study for the European Supply Chain Till 2030

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