Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks

Sarker, Bhaba R.; Wu, Bingqing; Paudel, Krishna P.

doi:10.1016/j.apm.2017.11.010

Cited by 26 publications

(5 citation statements)

References 36 publications

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“…Sarker et 10.11113/mjfas.v20n2.3085 al. [9] and [10] solved a location-allocation problem of the bio-methane gas supply chain constituted by multiple residues, hubs, and reactors. Saadati and Hosseininezhad [11] designed a bagasse-based bioethanol supply chain for locating hubs by considering road and rail transport.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Fong Yeng,

Zainuddin,

See Pheng

2024

Mal. J. Fund. Appl. Sci.

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Malaysia can convert agricultural wastes (biomass) into biofuel to reduce fossil fuel dependency and solve the disposal problem. As one of the largest palm oil producers, Malaysia has an abundance of palm oil biomass, but the biomass has high humidity, low energy density, and is scattered geographically. Establishing collection facilities with pretreatment operations is suggested to collect the biomass and improve its quality. Nevertheless, the facility placement and vehicle routing decisions significantly affect the total cost and operational efficiency. Hence, this study develops a model to address the location-routing problem and quantifies the pretreatment operation to customize the process in the biomass supply chain. This research also addresses sustainability from all dimensions through multi-objective optimization. The model minimizes costs, reduces negative social impacts by considering population densities, and measures environmental performance through CO2 emissions. The study first optimized each objective function separately and then conducted a multi-objective optimization using a weighted sum approach. Optimizing each objective function individually will achieve the best outcome for each dimension, but enhancing one objective would impair the others. However, multi-objective optimization shows some compensation for the performances where economic, social, and environmental indicator values decreased by 0.36%, 6.58%, and 15.28%, respectively. The results demonstrate that the model adjusts the locational and routing decisions based on different goals.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Fong Yeng,

Zainuddin,

See Pheng

2024

Mal. J. Fund. Appl. Sci.

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“…One of the critical aims in the context of the biogas subject is where to establish the biogas production sites and charging stations. A location problem of biogas reactors was studied in a work [6]. They proposed a mixed-integer nonlinear problem for a biogas supply chain system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Derse

Göçmen

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Environmental concerns prompt the world for a transition to renewable energy sources from fossil energy. Reducing the dependency on non-renewable sources of energy is prerequisite for the sustainable world and less environmental pollution. Biogas energy, which is one of the most important renewable energy sources, is produced by burning organic wastes and can be used in many different fields. In this study, a two-stage approach is undertaken to optimize a biogas supply chain design by incorporating of 30 districts in Izmir. In the first stage, the selection of the most suitable biogas plants is considered by the goal programming approach, which is of great importance to decide the optimal location with high energy potential. The most suitable sites for the biogas plants are obtained as Konak and Narlıdere districts. In the second stage, the location problem of the biogas vehicle charging stations (BVS) for biogas vehicles is handled considering the results of the first stage using mixed integer linear programming (MILP) approach. Computational results demonstrate that it would be more appropriate to establish BVS in 12 districts of İzmir. The model and solution approach are pioneering for supply chain problems and an efficient tool for renewable energy plans.

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“…Kumar et al reported that adaptive large neighborhood search (ALNS), a heuristic algorithm, took 140 s while CPLEX took ~3 h to find the solution for the same BSC problem (Kumar et al, 2015). A few studies showed a longer computational time comparing heuristic‐based optimization (that does not have a commercial solver) with commercial solvers such as LINGO (Sarker et al, 2018, 2019; Wu et al, 2015). However, compared with open‐source mixed‐integer non‐linear programming (MINLP) solvers, BONMIN and NOMAD, heuristics can achieve better values for the objective functions (Sarker et al, 2018, 2019; Wu et al, 2015).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

“…A few studies showed a longer computational time comparing heuristic‐based optimization (that does not have a commercial solver) with commercial solvers such as LINGO (Sarker et al, 2018, 2019; Wu et al, 2015). However, compared with open‐source mixed‐integer non‐linear programming (MINLP) solvers, BONMIN and NOMAD, heuristics can achieve better values for the objective functions (Sarker et al, 2018, 2019; Wu et al, 2015). However, all three studies were conducted by the same authors and this advantage of heuristics was reported for similar biogas systems.…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks

Cited by 26 publications

References 36 publications

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

Optimizing Palm Oil Biomass Supply Chain Logistics through Multi-Objective Location-Routing Model

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

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