Green, Yellow, and Woody Biomass Supply-Chain Management: A Review
Abstract
:1. Introduction
2. Supply-Chain Management Definition
3. Review Methodology
- Step 1: Development of the review protocol in terms of the eligibility criteria considering a single published research article as the set analysis unit.
- Step 2: Search for research studies and select the ones satisfying the eligibility criteria.
- Step 3: Definition of the classification framework to be applied in the literature review in order to classify the material and build the structure. Four classes (green, yellow, woody, and multiple-type biomass) are applied here with two sub-classes (i.e., the problem-based class and the methodology/approach-based class).
- Step 4: Selection of studies to be included in each classification within the framework.
- Step 5: Analysis of the selected studies and creation of a short summary of each individual work allocated to the corresponding class.
- Step 6: Representation of results by studies comparison.
3.1. Eligibility Criteria
3.2. Information Sources
4. Biomass-Type Classification Frameworks and Analysis
4.1. Green Biomass
4.1.1. Strategic Planning
4.1.2. Operational Planning
4.2. Yellow Biomass
4.2.1. Strategic Planning
4.2.2. Operational Planning
4.3. Woody Biomass
4.3.1. Strategic Planning
4.3.2. Operational Planning
4.4. Multiple Biomass
4.4.1. Strategic Planning
4.4.2. Operational Planning
5. Methodological Approach-Based Classification Framework
6. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Cited Work | Crop(s) | Methods/Models | Criterion | Data Sets | Time Frame |
---|---|---|---|---|---|
[7] | Energy beet | Simulation | Maximization of profit of potential ethanol plants | Yields, production, and transportation costs, agricultural opportunity costs, ethanol production, and ethanol prices | N/A |
[27] | Maize | Experimental approach evaluated by statistical analysis | Evaluation of biomass and biogas yields | Plant growth, yields, biogas, and methane yields | 3 years |
[17] | Triticale | Techno-economic computational model | Reduction of feedstock procurement operational costs, secured feedstock availability, and increased high-quality biomass supply | Triticale production data and biomass yields, commercial machinery data | 1 year |
[9] | Panicum virgatum | Two-phase simulation location-allocation modeling approach | Minimization of operational cost of the supply chain | Field, weather, and soil data sets, cost, demand, and price data and actual transport-related data | 1 month |
[18] | Wild perennial crops | Experimental approach together with statistical analysis | Biomass yields, biomass quality, and water-use efficiency | Meteorological data, soil water data, crop-related data | 4 years |
[10] | Miscanthus | Four-step assessment framework (i.e., design, optimize, assess, and compare biomass supply chains) | Economic optimization | Field and crop data, operational data, financial and energy-related data | 1 year |
[23] | Sugarcane, sugar beet, corn, rice and cassava | Experimental-based approach together with carbon flux assessment model | CO2 emissions | Crop-related data, soil data, carbon emissions data, etc. | 1 year |
[12] | Miscanthus and Panicum virgatum | An economic, biophysical, and GIS modelling approach | Supply-chain structure and prices | Yields, climate data, field-related data, etc. | 15 years |
[11] | Miscanthus | Simulation | Energy requirements assessment | Operational data, machinery data, energy input, crop data, etc. | 10 years |
[15] | Arundo donax | A computational approach on economics and energy consumption based mainly on experimental data | Cost-effectiveness and environmental sustainability | Financial data, operating data, machinery and transport data, etc. | Annual |
[14] | Miscanthus | A multiple regression modeling approach together with remote-sensing modeling approaches and experimental data | On-farm productivity | Soil water, climate data, georeferenced data, crop growth data, field and crop data, etc. | ≥ 5 years |
[26] | Sugar beet and sweet sorghum | Simulation and experimental-based approach | Energy performance | Field and crop data, meteorological data, machinery data, operational data, etc. | 3 years |
[25] | Arundo donax | Experimental approach and statistical linear model | Biomass yield and quality | Climate data, productivity, and biometrical data, soil, site and crop data, etc. | 2 years |
[22] | Panicum virgatum | Multi-criteria decision analysis technique | Reduction of economic losses and maximization of environmental performance | Soil and crop data, profitability data, climate data, operational data, etc. | 1 year |
[8] | Miscanthus, Panicum virgatum, and Arundo donax | Binary and linear programming simulation models | Optimal energy performance | Crop and fields data, machinery data, material data, energy coefficients, etc. | 10 years |
[13] | Miscanthus | Simulation | Yield and N content | Crop and field features, soil, climate, agricultural practices, etc. | 4–20 years |
[16] | Sugarcane | Simulation | Cost, energy, and emissions | GIS data, weather data, farm data, operational data, etc. | Annual |
[19] | Corn silo, wheat, and rapeseed | Web-based simulation tool | Energy balance | Fields and crop data, machinery and operational data, production means, etc. | N/A |
[24] | Miscanthus, Panicum virgatum, Arundo donax, and cardoon | Simulation | Environmental impact | crop data, operational, production means, weather data, etc. | 15 years |
[20] | Miscanthus, Panicum virgatum and Arundo donax | Computational simulation tool | Energy cost | Fields and crops data, machinery and operational data, transportation, production means, and materials, etc. | 10 years |
[28] | Miscanthus and Panicum virgatum | Simulation | Energy consumption savings | Field and crop data, machinery data, operational data (turning radii, operating width), energy input, etc. | Annual |
Cited Work | Type of Crop Residue(s) | Methods/Models | Criterion | Data Sets | Time Frame |
---|---|---|---|---|---|
[29] | Corn stover and wheat straw | Multiple linear regression and artificial neural networks (ANN) model | Crop residues availability, identification of optimal plant locations, and cost minimization | Crop yield, soil-related data, operational and financial data | Annual |
[32] | Corn stover | Open LCA software-based modeling approach and Monte Carlo simulation | Environmental impact of supply chain versus densification and pelletization | Field trials and crop data, operational data, emission, and biomass related data | Annual |
[31] | Wheat straw | Simulation-based approach | Minimum ethanol plant capital and operating costs in parallel with maximum profitability | Financial and environmental data, operating data | 20 years |
[33] | Cotton stalks | An integrated GIS and ANN prediction modeling approach and linear programming models | Yellow biomass availability, optimal plant location with the minimum supply-chain cost | Crop data, transport network data, weather data, geospatial data | 5 years |
[30] | Corn stover and wheat straw | Analytical modeling approach and simulation | Minimization of costs and environmental impact and maximization of energy efficiency | residual potential, machinery data, transport data, fields data, etc. | N/A |
[34] | Cotton stalks | Analytical approach and simulation | Minimization of total delivered cost and energy input | Crop and field data, machinery data, operational data, etc. | Annual |
[35] | Corn stover, sugarcane bagasse, sorghum straw, agave residue, wheat straw, rice straw, barley residue, and pecan nut shell | Geospatial optimization modeling approach | Optimal biomass processing location under uncertainty parameters | Spatial data, residual data, climatic data, terrain, and crop data | 5 years |
[36] | Cocoa crop residues | An integrated GIS-based fuzzy analytic hierarchy process (AHP) programming approach | Biomass availability, transportation, and slope feasibility | Crop yields, geospatial and land data, road data, accessibility, natural hazard, etc. | Annual |
[39] | Olive trees pruning residues | Experimental approach based on LiDAR (light detection and ranging) technique | Quantification of olive trees pruning | Dendrometric data, field data, LiDAR data, etc. | N/A |
Cited Work | Crop(s) | Methods/Models | Criterion | Data Sets | Time Frame |
---|---|---|---|---|---|
[46] | Poplar | Simulation and experimental-based approach | GHG emissions | Field and crop data, operational and machinery data, emissions inputs, etc. | 20 years |
[47] | Poplar SRC | Modeling approach by using SimaPro tool and by using experimental data | Environmental and energy performance, economic viability | Yield and crop data, operational data, machinery data, energy and emissions input, economics, etc. | 12 years |
[43] | Eucalyptus | Financial analysis of different experimental silvicultural practices evaluated by Monte Carlo simulation | Investment analysis criteria such as net present value, internal rate of return, and profitability | Experimental, various economical and statistics-related data | 3 years |
[44] | Willow | IBSAL simulation model | Highest performance of the integrated system based on parcel size, field shape, crop yield, storage location, and collection equipment | Field trials data and commercial machinery data | 5 years |
[48] | N/A | Experimental-based approach combined with linear programming modeling | Energy requirements and CO2 emissions | Vehicles-machinery data, road/traffic data, operational data | 2 months |
[40] | Eucalyptus | A computational approach based on WISDOM database and by using the network analyst tool | Minimization of costs and GHG emissions | Machine productivity data, operational data, yields, emissions data, road network data, | Annual |
[41] | SRC Willow | Discrete event simulation modeling | Cost effectiveness | Weather data, transport data, yields, geographical conditions, soil water content, storage-related data, machinery data | Annual |
[42] | Poplar SRC | Experimental approach based on growth model MoBiLE-PSIM and Umberto Software | Environmental impacts | Operating data, machinery data, field and crop data, growth data, etc. | 20 years |
[45] | Fruit trees | Mobile application development and performance approach | Biomass availability matching with heating energy requirements of agro-industrial buildings | Yields, energy requirements, climatic data, etc. | N/A |
[38] | Fruit tree, vineyards, and olive grove prunings and branches from up-rooted trees | Smart Logistics System Prototype development and performance | Optimization of supply-chain performance | Biomass-related data, spatial data, weather data, etc. | N/A |
Cited Work | Crop(s)/Residues | Methods/Models | Criterion | Data Sets | Time Frame |
---|---|---|---|---|---|
[37] | Woody and agricultural residues | Simulation and genetic algorithm development | Cost of transportation routes | Spatial data, yields, transportation and operating data, storage, etc. | N/A |
[56] | N/A | Simulation and regression models | Logistics efficiency | Spatial data, crop and field data, transportation, etc. | N/A |
[57] | N/A | Simulation | Logistics distances | Field and crop data, operational data, spatial data, transportation, etc. | N/A |
[58] | N/A | Scheduling algorithm development | Field readiness | Crop and field data, operational data, transportation, etc. | N/A |
[55] | N/A | Simulation | Task times and cost performance | Field data, transportation, operational data, machinery data, economics, etc. | N/A |
[51] | Potential for up to 30 listed biomass crops | Web tool development | Yield and cost | Production data, fields and crops data, economics data, machinery, and operational data, etc. | N/A |
[21] | Combination of various crops and crop residues | Experimental and simulation modeling | Productivity and resource-use efficiency | Data related to crop growth, field, weather and soil water | 2 years |
[52] | Various crops and crop residues | Mixed-integer linear programming optimization model | Minimization of the entire cost of the integrated bioethanol supply chain | Regional statistical data, crop-related data, operational cost data | N/A |
[49] | Triticale, sorghum, and Miscanthus | Simulation-based approach | Optimal feedstock supply-chain strategic planning based on agro-ecosystem modeling | Data related to weather, crop management, soil, emissions, operations, etc. | 20 years |
[50] | Residues and energy crops | Simulation-based approach | Farmer’s profitability and environmental sustainability | Crop rotations data, field-related data, soil data, weather data, operational and financial data | 5 years |
[53] | N/A | CyberGIS-enabled decision support platform development | Supply chain optimization | Spatial, agricultural (yields, production costs), and engineering/technology related data (such as transportation and operating data, etc.) | N/A |
[54] | Corn stover and Panicum virgatum | Simulation | Minimization of cost and energy consumption | Machinery data, operational data, financial and energy data, yields, etc. | 10 years |
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Rodias, E.; Berruto, R.; Bochtis, D.; Sopegno, A.; Busato, P. Green, Yellow, and Woody Biomass Supply-Chain Management: A Review. Energies 2019, 12, 3020. https://doi.org/10.3390/en12153020
Rodias E, Berruto R, Bochtis D, Sopegno A, Busato P. Green, Yellow, and Woody Biomass Supply-Chain Management: A Review. Energies. 2019; 12(15):3020. https://doi.org/10.3390/en12153020
Chicago/Turabian StyleRodias, Efthymios, Remigio Berruto, Dionysis Bochtis, Alessandro Sopegno, and Patrizia Busato. 2019. "Green, Yellow, and Woody Biomass Supply-Chain Management: A Review" Energies 12, no. 15: 3020. https://doi.org/10.3390/en12153020
APA StyleRodias, E., Berruto, R., Bochtis, D., Sopegno, A., & Busato, P. (2019). Green, Yellow, and Woody Biomass Supply-Chain Management: A Review. Energies, 12(15), 3020. https://doi.org/10.3390/en12153020