An integrated user-friendly web-based spatial platform for bioenergy planning

Tauro, Raúl; Rangel, Roberto; Suárez, Rodrigo Sarmiento; Caballero, José Luis Castrejón; Anaya-Merchant, Carlos; Salinas-Melgoza, Miguel Ángel; Guzmán, Héctor; Ghilardi, Adrián

doi:10.1016/j.biombioe.2020.105939

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The design of the DSS is applied to accommodate users' needs in obtaining information for decision-making [45,46]. DSS is expected to be able to contribute to the actors involved in the bioenergy supply chain, namely farmers, bioenergy industry owners, stakeholders, and stakeholders [47]. We propose adding IoT technology to DSS to increase the effectiveness and flexibility of DSS from bioenergy supply chains that have not been widely implemented in developing countries in improving decisionmaking in bioenergy supply chains.…”

Section: Figure 13 Proposed Model Of Bioenergy Supply Chainmentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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This study aims to design a decision support system (DSS) development model in sustainable bioenergy supply chains. Our approach involves 1) identifying the bioenergy supply chain model through determining the spatial potential model of agroindustry area using geographic information system (GIS) and analytical hierarchy process (AHP); 2) determining the optimization model for aggregate production process planning using fuzzy goal programming for bioenergy production to design the biomass inventory level determination model using adaptive neuro-fuzzy inference system (ANFIS) approach; 3) designing the concept of DSS model in bioenergy supply chain. The results showed the identification of the supply chain model from the spatial model of potential agroindustry locations with three regional categories: 19.34% potential, 16.93% not potential, and 63.70% developing. Aggregate planning is appropriate based on three objective functions to be achieved in production planning for determining inventory levels using ANFIS using three input variables and comparing performance with RMSE, MAPE, and R2 inventory levels so that the model can predict inventory levels adaptively. The concept of the DSS Model on the bioenergy supply chain from agricultural centers to users by adding Internet of Things (IoT) technology can increase the effectiveness and efficiency of the bioenergy supply chain. The managerial implications of this research can provide relevant insights for the design and improvement of renewable energy management programs. Utilization of local biomass resources becomes more optimal.

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Section: Figure 13 Proposed Model Of Bioenergy Supply Chainmentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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“…Creating a balance between customer expectations, and capabilities of the forest biomass and bioenergy SCs is one of the most important factors in impact on sustainability level against uncertainty and changeability of the marketplaces (Dashtpeyma & Ghodsi, 2021). In fact, targeting, planning and activities should be implemented by considering the customers feedbacks to efficiently deal with difficulties in the turbulent business environment (NWANKWO, 1993; Tauro et al, 2021). Therefore, by prioritizing the logical needs and demands of real customers that leads to an optimal customer orientation performance, a more resilient forest biomass and bioenergy SC can be achieved.…”

Section: Customer Orientation Performancementioning

confidence: 99%

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

Dashtpeyma¹,

Ghodsi²

2023

Global J. Eng. Technol. Adv.

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In recent years, with the increasing concerns about the global warming, limited non-renewable energy sources, and uncertainty in renewable energy sources accessibility, how to continuously supply clean sources of energy in form of a resilient system structure has become a topic of intense scholarly interest. Based on the milestone literature on the research field of renewable and non-renewable energies, forest biomass and bioenergy have significant compatibility with both industry and nature. Through this, our proposal contributes to supply one of the most available clean energy sources and direct this type of energy needed in an optimal way. The essential resilience components are determined and classified according to the theoretical and practical concepts. Then, this research is made use of a conceptual decision-making model based on third order confirmatory factor analysis to explore the interactions among the components in form of a network. The results reveal that the proposed model has sufficient validity and reliability levels. Finally, key interactions among the elements of model that contributed to most of the impacts on the forest biomass and bioenergy supply chain resilience were found to be optimized constantly within the network.

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“…En este trabajo se utiliza primeramente la plataforma geoespacial del Clúster de Biocombustibles Sólidos de México (Biowep, https://www.wegp.unam.mx/cemie/Mexico) (Tauro et al, 2021), la cual permite develar para cada recurso biomásico y área de interés, los diferentes centros de acopio y centrales de uso final (i.e. nodos).…”

Section: Introductionunclassified

Optimización de costos de transporte para el aprovechamiento energético de la biomasa

Tauro

Santibáñez-Aguilar

Heras

et al. 2022

regeo

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El aprovechamiento de la biomasa con fines energéticos es una alternativa atractiva para reducir la dependencia de los combustibles fósiles y su impacto ambiental. En este sentido, la ubicación geográfica de los sitios de producción y consumo de biomasa, su logística, así como la planificación de la cadena productiva, son factores indispensables para la evaluación y puesta en marcha de proyectos biomásicos con fines energéticos. Aunque diversos estudios han abordado estos problemas de forma separada, son pocos los trabajos que integran todos estos aspectos. En este trabajo se describe la integración de una serie de análisis espaciales, con un modelo matemático para la optimización de costos de transporte entre diferentes nodos de la cadena productiva de la biomasa. Mediante un caso de estudio y el análisis de diferentes escenarios de oferta y demanda, se evalúa el funcionamiento del módulo de optimización. Los resultados obtenidos permiten estimar de manera óptima la distribución de biomasa desde diferentes centros de oferta (nodos de almacenamiento), hacia diferentes usuarios (nodos de demanda), tomando al costo de transporte como función objetivo. La distribución óptima de biomasa permite planificar su uso para diferentes regiones de interés a nivel nacional.

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An integrated user-friendly web-based spatial platform for bioenergy planning

Cited by 8 publications

References 13 publications

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Forest biomass and bioenergy supply chain resilience: A conceptual decision-making model

Optimización de costos de transporte para el aprovechamiento energético de la biomasa

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