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For: Arabi M, Yaghoubi S, Tajik J. Algal biofuel supply chain network design with variable demand under alternative fuel price uncertainty: A case study. Computers & Chemical Engineering 2019;130:106528. [DOI: 10.1016/j.compchemeng.2019.106528] [Cited by in Crossref: 18] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Maharana D, Kommadath R, Kotecha P. A mixed-integer linear programming model with multi-unit strategy for distributed biorefinery superstructures with economic and social benefits. Clean Techn Environ Policy. [DOI: 10.1007/s10098-022-02296-z] [Reference Citation Analysis]
2 Sharifi M, Hosseini-motlagh S, Samani MRG, Kalhor T. Novel resilient-sustainable strategies for second-generation biofuel network design considering Neem and Eruca Sativa under hybrid stochastic fuzzy robust approach. Computers & Chemical Engineering 2020;143:107073. [DOI: 10.1016/j.compchemeng.2020.107073] [Cited by in Crossref: 15] [Cited by in F6Publishing: 7] [Article Influence: 7.5] [Reference Citation Analysis]
3 Mottaghi M, Bairamzadeh S, Pishvaee MS. A taxonomic review and analysis on biomass supply chain design and planning: New trends, methodologies and applications. Industrial Crops and Products 2022;180:114747. [DOI: 10.1016/j.indcrop.2022.114747] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Liu B, Yuan Z. Multistage Distributionally Robust Design of a Renewable Source Processing Network under Uncertainty. Ind Eng Chem Res 2021;60:7883-903. [DOI: 10.1021/acs.iecr.1c00446] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Yadala S, Smith JD, Young D, Crunkleton DW, Cremaschi S. Optimization of the Algal Biomass to Biodiesel Supply Chain: Case Studies of the State of Oklahoma and the United States. Processes 2020;8:476. [DOI: 10.3390/pr8040476] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
6 Thanigaivel S, Priya A, Senthil Kumar P, Kuan Shiong K, Hoang TK, Rajendran S, Soto-moscoso M. Exploration of effective biorefinery approach to obtain the commercial value-added products from algae. Sustainable Energy Technologies and Assessments 2022;53:102450. [DOI: 10.1016/j.seta.2022.102450] [Reference Citation Analysis]
7 Ching PML, Mayol AP, San Juan JLG, Calapatia AM, So RHY, Sy CL, Ubando AT, Culaba AB. AI Methods for Modeling the Vacuum Drying Characteristics of Chlorococcum infusionum for Algal Biofuel Production. Process Integr Optim Sustain 2021;5:247-56. [DOI: 10.1007/s41660-020-00145-4] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
8 Fazli-khalaf M, Naderi B, Mohammadi M, Pishvaee MS. The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry. Environ Dev Sustain 2021;23:9949-73. [DOI: 10.1007/s10668-020-01041-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
9 Zarei M, Niaz H, Dickson R, Ryu J, Liu JJ. Optimal Design of the Biofuel Supply Chain Utilizing Multiple Feedstocks: A Korean Case Study. ACS Sustainable Chem Eng 2021;9:14690-703. [DOI: 10.1021/acssuschemeng.1c03945] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
10 Zerafati ME, Bozorgi-Amiri A, Golmohammadi AM, Jolai F. A multi-objective mixed integer linear programming model proposed to optimize a supply chain network for microalgae-based biofuels and co-products: a case study in Iran. Environ Sci Pollut Res Int 2022. [PMID: 35301627 DOI: 10.1007/s11356-022-19465-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Kokkinos K, Karayannis V, Moustakas K. Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes. Front Energy Res 2021;8:622210. [DOI: 10.3389/fenrg.2020.622210] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
12 Afkhami P, Zarrinpoor N. The energy-water-food-waste-land nexus in a GIS-based biofuel supply chain design: A case study in Fars province, Iran. Journal of Cleaner Production 2022;340:130690. [DOI: 10.1016/j.jclepro.2022.130690] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Samani MRG, Hosseini-motlagh S. A mixed uncertainty approach to design a bioenergy network considering sustainability and efficiency measures. Computers & Chemical Engineering 2021;149:107305. [DOI: 10.1016/j.compchemeng.2021.107305] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Kalhor T, Sharifi M, Mobli H. A robust optimization approach for an integrated hybrid biodiesel and biomethane supply chain network design under uncertainty: case study. Int J Energy Environ Eng. [DOI: 10.1007/s40095-022-00513-5] [Reference Citation Analysis]
15 Maharana D, Kommadath R, Kotecha P. An innovative approach to the supply-chain network optimization of biorefineries using metaheuristic techniques. Engineering Optimization. [DOI: 10.1080/0305215x.2022.2080204] [Reference Citation Analysis]
16 Ahn Y, Kim J. Economic design framework of microalga-based biodiesel supply chains under uncertainties in CO2 emission and diesel demand. Computers & Chemical Engineering 2021;155:107538. [DOI: 10.1016/j.compchemeng.2021.107538] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]