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Sustainability
  • Review
  • Open Access

25 March 2022

A Review of Trends in the Energy Use of Biomass: The Case of the Dominican Republic

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and
1
Basic Sciences, Instituto Tecnológico de Santo Domingo, Santo Domingo 10602, Dominican Republic
2
Centre for Energy Studies and Environmental Technologies (CEETA), Universidad Central “Marta Abreu” de las Villas, Carretera a Camajuaní Km 5 ½, Santa Clara 50100, Cuba
3
Engineering Area, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo 10602, Dominican Republic
*
Author to whom correspondence should be addressed.

Abstract

This review examines the use of residual biomass as a renewable resource for energy generation in the Dominican Republic. The odology includes a thorough examination of scientific publications in recent years about logistics operations. The use of mathematical models can be beneficial for the selection of areas with a high number of residual biomass and processing centers; for the design of feedstock allocation; for the planning and selection of the mode of transport; and for the optimization of the supply chain, logistics, cost estimation, availability of resources, energy efficiency, economic performance, and environmental impact assessment. It is also essential to consider the exhaustive analysis of the most viable technological solutions among the conversion processes, in order to guarantee the minimum emissions of polluting or greenhouse gases. In addition, this document provides a critical review of the most relevant challenges that are currently facing logistics linked to the assessment of biomass in the Dominican Republic, with a straightforward approach to the complementarity and integration of non-manageable renewable energy sources.

1. Introduction

Climate change and the increase in world population have increased the global level of environmental degradation and depletion of natural resources. These challenges prompt the need for a transition towards efficient production, consumption in the use of resources, the reduction in and recovery of waste streams, and the transformation required of consumption habits [1]. The most widely used energy sources on the planet are fossil fuels, particularly oil. At present, these fuels are the most widely used globally and drive the economies of the wealthiest countries [2].
The combustion of fossil fuels inherently produces and releases harmful chemicals into the environment, in addition to causing other environmental burdens; therefore, it is essential to move towards the use of renewable energy resources to minimize the environmental impacts of fossil fuels [3,4]. Given the magnitude of the problems that arise with conventional energy sources, agreements have been generated, such as the Paris Agreement in 2015 [5], aimed at counteracting climate change, making it clear that, it must contemplate and include the use of renewable energy sources to transition away from fossil fuels [6].
Among these energy sources stands out biomass, which has forever been used by humanity for heat and lighting [7,8]. In the total primary energy supply, fossil fuels represent 81%, nuclear energy represents 5%, and renewable energy sources represent 14% (of this, biomass contributes around 70%) [9].
This article aims to systematically compile the most important trends in the use of residual biomass as a renewable energy resource, focusing on the Dominican Republic. Specifically, all aspects of the supply chain will be addressed, highlighting logistical aspects [10,11,12,13,14,15,16], optimization models [13,14,15,17,18,19,20,21,22,23,24,25,26,27], and geographic information systems [18,19,20], as well as their significance, to make the decision-making process easier [17,28,29,30], referring to the optimization of the use of biomass in terms of availability, cost, and quality; conversion performance; transportation; and storage costs. Analysis is carried out by grouping the collected scientific material around environmental impact, supply chain, costs, and biomass conversion processes.
The main contributions of this work are the following:
  • A critical review of trends in logistics operations linked to biomass conversion into energy;
  • Identification of relevant factors and their solutions for optimizing resources to impact cost reduction;
  • A detailed discussion of the factors identified in the literature and their relationship in the context of the Dominican Republic, to facilitate the integration and reduction in intermittence generated by non-manageable renewable energy sources.
This document is structured as follows: Section 2 describes the relevance and advantages of using biomass as a source of energy; Section 3 analyzes approaches to technological solutions for energy generation; Section 4 presents research trends in the energy use of biomass. Section 5 examines our conclusion and challenges.

2. Energy Use of Biomass

Due to rising oil prices, increased agricultural production, climate change, and new methods of obtaining energy, biomass has resurged as an energy source [31].
Over time, supply concerns, as well as the gradual concern both about achieving sustainable development and mitigating climate change, have emerged. These facts have prompted the international community to advance on global and regional initiatives in support of the introduction of renewable energy sources [32].
As such, the approaches to eliminating greenhouse gases are considered essential in several projections to meet the ambitions set out in the Paris Agreement [33]. Additionally, public acceptance is one of the main factors influencing local utilization of renewable energy [34].
In a productive oil based economy, as in the case of the Dominican Republic, the increase in the cost of fossil fuels, in combination with the detrimental environmental impact, has led authorities to adopt commitments aimed at promoting renewable energies. In particular, promoting biomass generates advantages, as it is a diverse, manageable source with broad technological applications with neutral emissions of carbon dioxide (CO2) [35].
In the current environmental context, the Dominican Republic is committed locally and internationally to providing solutions towards a diversified matrix of renewable sources and reducing global greenhouse gas emissions. In this regard, reports from the Directorate of Alternative Sources and Rational Use of Energy of the National Energy Commission (CNE) refer to the need to take advantage of the existing biomass potential, taking into account the commitment to reach 300 MW of installed energy capacity by 2030 in the framework of the Paris Agreement [36].
The Dominican State has made evident its intention to define a diversified and renewable energy matrix, after the approval of Law 57-07 on “Incentives for the Development of Renewable Energy Sources and their Special Regimes” [37], which suggests the granting of incentives to develop energy generation projects from the use of wind, solar, and biomass. At present, large scale energy projects based on renewables have been developed at the national level, taking advantage of the benefits provided by such legislation.
San Pedro Bioenergy stands out among the projects that supply electricity to the National Interconnected Electric System, which has an installed capacity of 30 MW using sugarcane bagasse biomass as fuel. Monte Plata Solar is another project with sufficient capacity to generate 60 MW [38], and the Los Cocos and Larimar wind farms, owned by the company EGE Haina, contribute up to 175 MW. Despite the significant progress made, fundamental challenges still prevail, linked to offering a renewable and sustainable energy supply and other problems associated with aspects such as access to local financing, and the improvement of tax incentives that can cover a broader spectrum of the production chain. Other aspects include the formalization of the market, the contribution of subsidies, the quality of the energy supply, the protection of the producer, and the generation of scientific–technological information [38].
The Dominican Republic has exhibited traditional models for the use of biomass, for example, the generation of steam and electricity in sugar mills and the drying of rice in factories, which were not necessarily carried out within a framework of ecological sustainability. Environmental imperatives set by the headquarters of two free zone companies with large operations in the country, and the enactment of Law 57-07, became catalysts for developing an incipient biomass market [36].
Faced with a growing demand for biomass resulting from the substitution of conventional boilers for biomass boilers for industrial processes, the National Energy Commission of the Dominican Republic saw an excellent opportunity for growth in the market for this energy source. The orderly growth of the biomass business within a sustainable development strategy would not be possible without a study that establishes a baseline of the market (current production) and potential production and the characterization of the types of biomass. This is needed to have the necessary inputs for the quality regulations of this and a ten-year plan of the projected growth of the market [39]. All these considerations served as the basis to justify one of the most recent investigations carried out by the National Energy Commission [37], whose primary purpose was to analyze the current biomass production in the Dominican Republic, as well as its potential, in addition to defining a plan for the use of energy generation, based on obtaining the following products:
  • Baseline study of the biomass market for generation;
  • Geographical/spatial analysis of the areas with the most significant potential for biomass production;
  • Plans to promote the use of biomass for thermal and electric energy;
  • Existing laws, regulations, and regulations on biomass.
The demand estimate for the year 2030 for wind and solar energy is around 63% of the demand in real time. This would mean a third more of the wind energy and almost a quarter more of the solar energy used in recent years, thereby reducing natural gas and petroleum derived fuels by more than 25% [20]. In this projection, the need to install a battery as a frequency support is evident [40].
It is crucial to bear in mind that the natural variability of demand due to consumers’ unpredictable and instantaneous decisions is opposed by a generation that, while renewable energy is incorporated into the energy mix, behaves less manageably and dramatically complicates the operation of the electrical system. For a high contingent of non-manageable renewable energies to be safely integrated, it will be essential that the operation of the electrical system be equipped with a variety of tools that guarantee this [41].
The variability of feedstock requires the connection of backup generation with a manageable and sufficiently flexible nature, capable of absorbing the production variations derived from the intermittence, in terms of its presence, of the primary resource [41]. In the current scenario of the Dominican Republic, there is a biomass potential that is not sufficiently quantified from the energy point of view, as well as the dispatch and management conditions of this resource in electricity generation to cover the intermittency caused by the renewable energies already used, such as solar and wind.

2.1. Identification of Potentialities

Identifying potentials for the production and use of biomass refers to the evaluation and quantification of biomass potential in a particular locality, according to its different origins and possibilities for introduction into the energy market, considering the estimated costs for its production and market distribution. Resource evaluations are an indispensable element in the feasibility study for establishing a biomass plant, which requires in depth knowledge of the potential for biomass generation according to its nature, be it primary or secondary.
In this sense, biomass resource evaluations should allow estimating the potential, as well as the amount, of usable biomass in an area, even supplying valid information according to the desired level of detail; additionally, such evaluations should serve to determine the size or energy production capacity of a region or to make a decision about the location of a plant [28].
Therefore, a standard methodology is required to recognize potentially suitable areas for sustainable bioenergy crops. This methodology would better identify promising crops and cropping systems, and logistical and economic studies, and better estimate the work required to meet regulatory criteria [42]. Under the premises above, investigations have been directed to estimating the biomass potential in the Dominican Republic, which has made it possible to establish the different biomass resources available and their theoretical energy availability [41].
However, it is essential to clarify the maximum theoretical potential, including technical or economic factors. In addition to identifying the primary biomass resources present, such as sugar cane, coffee, rice, cocoa, and banana, these investigations delve into the possibility of planting energy crops for electricity generation. However, this alternative must be oriented appropriately, since, with an increase in energy production, crops would promote monoculture agricultural practices that could cause a variety of inconveniences with environmental and local impact [43]. It must also be considered that the main barriers in the development of biomass and biofuels are the high cost of the raw material, the lack of reliable supply, and uncertainties [44].
Under the conditions of the Dominican Republic, there is a potential for residual biomass derived from different agro-industrial processes, which, at the same time, constitute a residue that requires the producer to handle or treat it for its final disposal, which can be costly; however, its use for energy purposes is still limited. The energy potential of these wastes for producing electricity or other energy applications must be linked to the sectors that generate them, which would reduce the electricity demand at a local or country level. Furthermore, biomass could also serve as a strategic complement to intermittent renewable energies by supplying electricity during hours of high residual load [45] and thus achieving better stability during the supply of electrical energy and favoring the possibility of increasing the level of penetration of intermittent or nondispatchable renewable energy sources in the Dominican energy mix.
According to the Worldwatch Institute [46], integrating a multiplicity of renewable energy sources could achieve an even more significant reduction in the problems associated with the intermittency of renewable sources. Particularly in the case of the Dominican Republic, the combination of solar and wind generation in the grid could specifically contribute to reducing seasonal variability. In addition, the alternatives for storing electricity, especially those concerning batteries and hydraulic pumping systems, could be offset by the capacity of renewable energy for storing energy generated during periods of high production and low demand, to supply the network at peak hours. Collectively, the use of biomass power plants, fast on and off, in parallel with solar power plants, can also generate baseload energy.

2.2. Biomass Selection Criteria

Correctly choosing biomass in specific areas reveals the importance of using pertinent selection criteria. When choosing the most appropriate energy options, evaluation criteria should be developed that may be useful for decision-makers seeking the integrated performance of other alternatives [46]. Pohekar and Ramachandran further emphasize the importance of the selection criteria. Despite the widespread promotion of renewable energies for different applications, compared to improved technologies for energy production and more intense competitiveness with other conventional energies, the contribution of renewable energies is still considered modest [29].
Consequently, it is preferable to formulate different concepts, primarily associated with energy planning, so that decision-makers can identify and remove obstacles that prevent biomass energy from becoming a relevant source in the future.
Cost considerations still dominate discussions about climate protection and energy sector transition in the Dominican Republic. The expanded use of renewable energies and the application of energy efficiency restoration measures can positively impact local and regional added value and employment [47]. It is necessary to delve into the analysis of the potential, both in this agricultural sector and on an industrial scale, to establish the most suitable conversion routes by type of biomass, to allow the correct use of these and estimate their availability in temporary spaces.
The preceding should make it possible to evaluate complementarity with distributed electricity generation, considering that biomass is a manageable resource that can reduce the intermittency that occurs during generation in the electricity system, compared to other renewable sources of electricity. On the other hand, according to González and Muñoz, sustainability problems and technical–economic parameters linked to supply chain management must be considered [25].

2.3. Numerical Analysis Tools and Geographic Information Systems

Knowing the potential quantity of biomass in a territory is not the only factor considered in determining the viability of using this resource in energy applications [48,49]. There are factors of a spatial nature with decisive influence on its use, since they determine the cost of extraction, the distance from the source to the transport network, and other environmental conditions. The link of a spatial component is, evidently, related to the resource’s valuation and the optimal locations for its use. Numerical analysis technologies and geographic information systems (GIS) are tools that make it possible to assess the complexity of biomass resources and define the most relevant factors from a territorial and cost-estimation point of view [50,51].
Alluding to this, some studies reflect the usefulness of using statistical data and GIS methods in estimating biomass resources and their bioenergy potential [52]. For their part, they reviewed the essential characteristics of biomass logistics operations, discussing how these were incorporated into mathematical optimization models, also explaining the new trends in their optimization [14]. One of the most critical aspects of biomass use is its supply chain and all the elements that are part of it; modeling is a powerful tool to improve its efficiency [53]. However, biomass models for the energy supply chain must include analyzing several different variables and highlighting the main disadvantages of their use [54]. In this regard, models and methods to optimize biomass supply chains have been analyzed, making a complete overview of research in this field focusing on optimization modeling problems and solution perspectives. Other tools, such as satellite, aerial, and terrestrial remote sensing, can be handy to monitor and estimate biomass to increase raw material production from energy crops and maximize their yield [55].
Due to the low demand for biomass by consumers and investors due to the high costs in its transformation for energy purposes, tools are required that allow optimal modeling of the supply chain of this resource. This can be implemented at the national and regional level, considering the abundance and accessibility of biomass in the Dominican Republic. These new optimization models motivate the exercise of a notable contribution to the revitalization of the agro-industrial and rural areas, promoting, in parallel, the achievement of the proposed goals in energy self-sufficiency and the replacement of fossil fuels by renewable energy [56].
Evaluating the effective and absolute magnitude of bioenergy resources, together with the development of geographic information systems that allow estimating their availability, location, ownership regime, and limitations of use, will promote the sustainable and efficient use of bioenergy sources in the country.
The use of biomass under planning and sustainability parameters will collaborate to maintain the region’s agricultural areas’ essential ecological, economic, and social functions. Few studies have been carried out on agro-industrial harvesting and logistics systems linked to biomass resources in the Dominican Republic. In this sense, of particular interest is the identification of existing residual biomass potentials and especially the valuation of land use to define areas that can be utilized for the proliferation of forest biomass and for the systems of geographic information, according to its application in similar studies [57].
On the contrary, the existence of conversion technologies for agricultural biomass, developed and in use, is notorious; due to this, the establishment of predictive models of the supply chain of this resource is imperative [58], which define a low cost bioenergy utilization system for agricultural biomass, in adaptation to the realities of the region. In general, in bioenergy, the Dominican Republic has an unlimited amount of agricultural residues and waste, which are residual sources with enormous potential that could meet the growing energy demand [59] and, in turn, increase the share of renewables.
Concerning the above, the region has set ambitious goals to reduce its per-capita greenhouse gas (GHG) emissions through residual biomass. However, its lower calorific value and lower density than mineral coal are disadvantages for the sustainable use of residual biomass as an energy source [60]. The estimation of the quantities of potential and existing biomass available represents the first parameter to be considered for determining the viability and convenience of using biomass in power generation and, in addition, the support of the support plans. This is vital to achieve an integrated and optimized use of the resources present in the territory for biomass management. The Dominican Republic lacks complete and scientifically developed studies on the actual quantification of mobilizable agricultural biomass resources for bioenergy use, both from a quantitative and qualitative perspective, with the support of the aforementioned tools.

3. Biomass as a Source of Energy Generation

Biomass can be used to meet a wide variety of energy needs, including generating electricity, supplying heat for industrial facilities, heating homes, and fueling vehicles, among other applications. The conversion of biomass to these valuable forms of energy can be achieved using various technological solutions that can be separated into two basic categories: thermochemical processes and biochemical/biological processes [11,61].
The options for biomass conversion processes are classified according to the type of final energy products, including thermochemical processes and chemical processes. Focusing on thermochemical processes, the leading technology solutions are as follows [62]:
  • Combustion converts biomass energy into heat, mechanical energy, or electricity. The net conversion efficiencies range from 20% to 40%, and even higher values are possible when biomass is burned in coal fired power plants. The most commonly used combustion chambers for biomass applications are fluid bed and hearth designs; the latter is rapidly becoming the technology of choice due to low nitrogen oxide emissions [62].
  • Gasification converts biomass into a fuel gas mixture of carbon monoxide, hydrogen, and methane, characterized by a low calorific value burned to produce heat and steam or used in gas turbine cycles to obtain electricity. Conversion efficiencies of up to 50% can be achieved in gasification using integrated biomass gasification/combined gas–steam cycles. Although many biomass gasification processes have been developed commercially, only fluid bed configurations are considered in applications ranging from 5 to 300 MW [62].
  • Pyrolysis is the conversion of biomass into a liquid fraction (bio-oil), a solid fraction (charcoal), and a gaseous fraction by heating the biomass in the absence of air [62].
Regarding biochemical processes, the primary conversion options include [62]:
4.
Fermentation is when the sugars released during enzymatic hydrolysis are fermented to carry out ethanol production, also producing carbon dioxide, butanol, organic acids, xylitol, and furfural [63]. This process uses microorganisms to convert a fermentable substrate into recoverable products, such as biomass, alcohols, and organic acids. Hexoses, especially glucose, represent the most assimilable substrate by microorganisms, while pentoses, glycerin, and other compounds need specific or modified organisms to convert possible [64].
5.
Anaerobic digestion converts biomass into biogas, composed mainly of methane and carbon dioxide, through bacterial action in the absence of oxygen. Anaerobic digestion is a commercially proven technology widely used to treat high moisture biomass [12,62].
6.
Another technology is represented by the mechanical extraction processes, capable of producing energy in biodiesel forms. However, it is only a part of the process that consists of transesterifying oils and fats with methanol in the presence of a catalyst. However, currently, the cost of biodiesel compared to fossil fuel makes this conversion uncompetitive; however, the increasing focus of government policies on achieving better air quality standards may rapidly change this perspective [12].
The choice of the appropriate conversion process is influenced by many key factors, such as the type and quantity of biomass resources, energy carriers and end use applications, environmental standards, and economic conditions. Likewise, it is essential to note that biomass resources include wood and wood residues, crops (i.e., short rotation woody crops, woody, herbaceous, sugar, and oilseed crops), byproducts, and solid residues. Municipal waste comes from agro-industrial and food processes, aquatic plants such as algae and water weeds, etc. [64].
In addition to the amount of energy potentially available from certain biomass species, other properties that dictate the most appropriate choice of the energy conversion process are represented by the moisture content, the cellulose/lignin ratio, and the ash content.
Regarding the cellulose/lignin ratio, this parameter only affects the biochemical conversion processes; particularly, biomass with a high proportion of cellulose instead of lignin—such as hardwood, which contains 25–50% cellulose and 20–25% lignin—is more compatible with fermentation processes. Finally, concerning the ash content, low percentages are preferred for thermochemical and biochemical processes because, given the available energy production of the adopted conversion technologies, the resulting amount of the final product is proportionally reduced [12]. How frequently energy is required drives the selection of the technological solution, then the type and amount of biomass available.
Despite the widely accepted potential of the advantageous use of bioenergy, the critical problems regarding biomass remain the limited availability in terms of time due to its seasonality and the dispersed geographical distribution in the territory, which makes the collection, transportation, and storage operations complex and expensive. These critical logistical aspects strongly affect bioenergy conversion systems’ economic and energy performance, introducing limitations on their suitability. In addition, the large number of the possible combinations of various biomass sources, the different conversion approaches available, and the various end use applications (power generation/heat and transport fuel), make it challenging to choose the optimal solution from a cost and power generation perspective [12].
Under these premises, this article presents a documentary review of recent research to determine the economic viability of the use of biomass for direct energy production through thermochemical conversion processes, considering the related technical, organizational, and logistical problems with the bioenergy chain. Thermal utilization processes have been chosen for analysis because they favor the direct production of electrical energy in a reasonably wide range of plant sizes, allowing centralized or decentralized applications that represent the most promising solutions for industrial applications from biomass to energy [11].

5. Conclusions and Recommendations

The most significant conclusions of this study are summarized based on the research trends exposed from the different perspectives addressed:

5.1. Environmental Impact

  • The dependence of today’s society on fossil fuel resources and their consequent negative influence on climate change has led to the rethinking of how energy is produced and consumed. The studies reiterate the emergence of serious environmental problems due to fossil fuels; thus, biomass has sparked growing interest as a promising renewable energy.
  • Despite the belief that wood and biomass combustion is entirely safe and does not have adverse effects on a social and environmental level, some research disputes this claim, arguing that biomass cannot be considered ecological even though it is a renewable fuel. Its emissions are too high and comparable to coal combustion, which is scientifically debatable. Hence, it is suggested to deepen the review of other studies to issue solid conclusions in this regard.
  • Solid biomass is the only source of renewable energy with practical use in the industrial field, so it is essential to identify its current employment in the sector to analytically project its future use and define global strategies for heat production. The pressure on the use of ecological energy resources in the industrial sector is increasing, so it is essential to consider their control and sustainability.

5.2. Supply Chain

  • The aforementioned mathematical models provide solutions to some problems related to the biomass supply chain, including selecting processing centers, the design of biomass allocation, the selection of the mode of transport, and the evaluation of the environmental impact in order to facilitate decision making.
  • Other models use geographic information systems and the spatial distribution of biomass and road maps to select areas with high availability located close to the conversion centers. These simulations improve the efficiency of biomass collection and precision in the location of the facilities, allowing decision makers to use the results for the expansion of the application of biomass in the energy sector.
  • Supply chain optimization is a tool that has been used to help the biomass industry gain a foothold; for this, models are also used to generate yield maps based on reference data on the quality of the available land, which more accurately suggest decision-making on the quantity and location of biomass growth operations.
  • Additionally, simulations that address the collection and delivery of full truckloads can provide helpful information on the impact of critical parameters of biomass logistics on routing results, increasing the efficiency of transportation planning processes.
  • Regarding biomass storage, it is necessary to consider that, to maximize the economic value of the fuel, supply chains must be managed so that moisture content is reduced to a minimum, for which various treatments are being tested to obtain more homogeneous fuels that generate reliable economic effects.

5.3. Costs

  • In addition to the environmental impact, it is pertinent to evaluate the technical and economic potentials of using biomass for energy purposes, among which energy saving, reduction in CO2 emissions, cost, and availability of resources stand out.
  • Woody biomass processing is one of the most viable alternatives for generating energy and reducing dependence on imports, offering opportunities to stimulate regional economies, especially in rural regions where development options in this sector are often limited.
  • Techno-economic analyses of biomass conversion plants can include measuring the economic performance, estimating the investment risk, and evaluating the environmental impact under special conditions concerning the project’s attractiveness.
  • Other analyses are oriented to the simulation of biomass processes that include the determination of energy efficiency, material consumption, total capital investment, production costs, and carbon taxes, and providing information on the improvement and operation of biofuel production.
  • The development of the biomass industry is suggested to be a motivating factor for the global circular economy and sustainability in developing countries; however, it faces a series of commercialization barriers and challenges, the analysis of which allows the generation of recommendations that cover the areas of technological innovation, logistics management, the interaction between academia and industry, policies and application, social impact, and international benchmarking.

5.4. Conversion Processes

  • Using coal with biomass as a complementary fuel in combustion or gasification processes is a viable technological option to reduce fossil fuel emissions. Some research shows the potential of biomass fuel and the scope of maximizing its proportion in the mix in coal based power generation plants and the benefits derived from it.
  • In biomass, gasification is considered a key technology, the promotion of which requires advanced, profitable, and highly efficient processes and systems. Hence, there is a need to investigate the concepts for the integration and combination of processes that aim to allow greater efficiency, better quality and purity of gas, and lower investment costs.
  • The joint combustion of biomass can have a very influential role in reducing greenhouse gas emissions, since it can reduce the possible environmental impacts associated with the combustion of fossil fuels; for this, it is necessary to study the main global biomass co-combustion initiatives and their perspectives to ensure the goal of renewable energy.
  • According to its potential as one of the cheapest routes to renewable liquid fuels, it is pertinent to analyze the challenges in using rapid pyrolysis of biomass.
Figure 1 shows that about 51% of the studies reviewed contemplate the entire supply chain for the optimization of its component processes, 57% of the biomass analyzed from the perspective of polluting emissions are linked to combustion and 55% of the biomass analyzed from the perspective of management cost is linked to agriculture and forestry. This information makes it possible to understand the trends linked to biomass studies.
Figure 1. Trend of studies linked to the biomass supply chain.
This research provides a simplified compilation of an invaluable body of knowledge that is particularly useful for evaluating the integration of renewable energy technologies in any economic sector based on the optimal use of residual biomass, for which it is used as a general procedure in the analysis and modeling of the logistics system due to its significant influence on the decision-making process.
It is necessary to develop methodologies that allow the identification of the natural stocks of bioenergy resources and their production potential at the country scale. In addition to carrying out studies aimed at optimizing the supply of biomass—in order to improve the management practices of its production, collection, and distribution systems, as well as the technologies for converting this resource into energy—the development of multipurpose management systems (mainly agricultural and forestry), the search for promising forest species, the development of efficient conversion technologies, and their profitability should be considered.

Author Contributions

Conceptualization, H.G.-B. and I.L.-D.; methodology, H.G.-B.; writing—original draft preparation, H.G.-B.; writing—review and editing, H.G.-B., M.A.-M. and J.A.d.F., supervision, J.A.d.F. and I.L.-D.; funding acquisition, INTEC. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Instituto Tecnológico de Santo Domingo (INTEC) grant number CBA-332202-2019-P- And The APC was funded by Instituto Tecnológico de Santo Domingo (INTEC).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors gratefully acknowledge Instituto Tecnológico de Santo Domingo (INTEC) for the financial support of this research.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

GHGgreenhouse gas
IRRinternal rate of return
PBPpayback period
NPVnet present value
CO2-ecarbon dioxide equivalent
Bblbarrel
ROIreturn on investment
USDUnited States dollar
MWmegawatt
GJgigajoules
TEAtechno-economic analysis
BSPbiomass steam processing

References

  1. Ahmadvand, S.; Khadivi, M.; Arora, R.; Sowlati, T. Bi-objective optimization of forest-based biomass supply chains for minimization of costs and deviations from safety stock. Energy Convers. Manag. X 2021, 11, 100101. [Google Scholar] [CrossRef]
  2. Fajardo-Ronquillo, V.P. Incidencia de la Caída de los Precios del Petróleo en la Economía Latinoamericana. Polo Del Conoc. 2020, 5, 1054–1067. [Google Scholar]
  3. Tarighaleslami, A.; Ghannadzadeh, A.; Atkins, M.; Walmsley, M. Environmental Life Cycle Assessment of a Cheese Production Plant towards Sustainable Energy Transition: Natural Gas to Biomass vs. Natural Gas to Geothermal. J. Clean. Prod. 2020, 275, 122999. [Google Scholar] [CrossRef]
  4. Xue, L.; Haseeb, M.; Mahmood, H.; Alkhateeb, T.T.Y.; Murshed, M. Renewable energy use and ecological footprints mitigation: Evidence from selected South Asian economies. Sustainability 2021, 13, 1613. [Google Scholar] [CrossRef]
  5. Nava, C. El acuerdo de París. Predominio del soft law en el régimen climático. Boletín Mex. De Derecho Comp. 2016, 147, 99–135. [Google Scholar] [CrossRef] [Green Version]
  6. Cano, J. Estudio de Caso: El Auge de las Energías Renovables; Institución Universitaria Politécnico Grancolombiano: Bogotá, Colombia, 2020. [Google Scholar]
  7. Casteló, A. Diseño de un Sistema Sostenible de Calefacción para una Vivienda Mediante una Energía de Biomasa; Universidad Politécnica de Valencia: Valencia, Spain, 2020. [Google Scholar]
  8. Ruiz, J. Análisis de la Problemática de Investigación de Aspectos Avanzados de la Generación Eléctrica con Biomasa. Ph.D. Thesis, Universidad de la Rioja, Barcelona, Spain, 2013. [Google Scholar]
  9. Popp, J.; Kovacs, S.; Olah, J.; Diveki, Z.; Balazs, E. Bioeconomy: Biomass and biomass-based energy supply and demand. New Biotechnol. 2021, 60, 76–84. [Google Scholar] [CrossRef] [PubMed]
  10. Ko, S.; Lautala, P.; Handler, R.M. Securing the feedstock procurement for bioenergy products: A literature review on the biomass transportation and logistics. J. Clean. Prod. 2018, 200, 205–218. [Google Scholar] [CrossRef]
  11. Caputo, A.C.; Palumbo, M.; Pelagagge, P.M.; Scacchia, F. Economics of biomass energy utilization in combustion and gasification plants: Effects of logistic variables. Biomass Bioenergy 2005, 28, 35–51. [Google Scholar] [CrossRef]
  12. McKendry, P. Energy production from biomass (part 2): Conversion technologies. Bioresour. Technol. 2002, 83, 47–54. [Google Scholar] [CrossRef]
  13. Soares, R.; Marques, A.; Amorim, P.; Rasinmäki, J. Multiple vehicle synchronisation in a full truck-load pickup and delivery problem: A case-study in the biomass supply chain. Eur. J. Oper. Res. 2019, 277, 174–194. [Google Scholar] [CrossRef]
  14. Malladi, K.T.; Sowlati, T. Biomass logistics: A review of important features, optimization modeling and the new trends. Renew. Sustain. Energy Rev. 2018, 94, 587–599. [Google Scholar] [CrossRef]
  15. Zhang, J.; Osmani, A.; Awudu, I.; Gonela, V. An integrated optimization model for switchgrass-based bioethanol supply chain. Appl. Energy 2013, 102, 1205–1217. [Google Scholar] [CrossRef]
  16. How, B.S.; Ngan, S.L.; Hong, B.H.; Lam, H.L.; Ng, W.P.Q.; Yusup, S.; Ghani, W.A.W.A.K.; Kansha, Y.; Chan, Y.H.; Cheah, K.W. An outlook of Malaysian biomass industry commercialisation: Perspectives and challenges. Renew. Sustain. Energy Rev. 2019, 113, 109277. [Google Scholar] [CrossRef]
  17. Shen, B.; Yang, K.L.H. Transportation decision tool for optimisation of integrated biomass flow with vehicle capacity constraints. J. Clean. Prod. 2016, 136, 197–223. [Google Scholar]
  18. Buffat, R.R.M. Spatio-temporal potential of a biogenic micro CHP swarm in Switzerland. Renew. Sustain. Energy Rev. 2019, 103, 443–454. [Google Scholar] [CrossRef]
  19. Charis, G.; Danha, G.; Muzenda, E. A review of the application of gis in biomass and solid waste supply chain optimization: Gaps and opportunities for developing nations. Detritus 2019, 6, 96–106. [Google Scholar] [CrossRef]
  20. Cintas, O.; Berndes, G.; Englund, O.; Cutz, L.; Johnson, F. Geospatial Supply–Demand Modeling of Biomass Residues for Cofiring in European Coal Power Plants; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2018. [Google Scholar]
  21. Larson a, J.; Yu, E.; English, B.; Jensen, K.; Gao, Y.W.C. Effect of outdoor storage losses on feedstock inventory management and plant-gate cost for a switchgrass conversion facility in East Tennessee. Renew. Energy 2015, 74, 803–814. [Google Scholar] [CrossRef] [Green Version]
  22. Morato, T.; Vaezi, M.K.A. Developing a framework to optimally locate biomass collection points to improve the biomass-based energy facilities locating procedure—A case study for Bolivia. Renew. Sustain. Energy Rev. 2019, 107, 183–199. [Google Scholar] [CrossRef]
  23. Zhu, X.; Li, X.; Yao, Q.; Chen, Y. Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry. Bioresour. Technol. 2011, 102, 1344–1351. [Google Scholar] [CrossRef]
  24. Cardoso, J.; Silva, V.E.D. Techno-economic analysis of a biomass gasification power plant dealing with forestry residues blends for electricity production in Portugal. J. Clean. Prod. 2019, 212, 7412753. [Google Scholar] [CrossRef]
  25. González, M.; Ruiz, M.; Del Campo, A.; Garcia, A.; Francés, F.y.L.C. Managing low productive forests at catchment scale: Considering water, biomass and fire risk to achieve economic feasibility. J. Environ. Manag. 2019, 231, 653–665. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Nicoletti, J.; Ning, C.Y.F. Incorporating agricultural waste-to-energy pathways into biomass product and process network through data-driven nonlinear adaptive robust optimization. Energy 2019, 180, 556–571. [Google Scholar] [CrossRef]
  27. Li, T.; Thunman, H.S.H. A fast-solving particle model for thermochemical conversion of biomass. Combust. Flame 2020, 213, 117–131. [Google Scholar] [CrossRef]
  28. Rodríguez, L.; Bretón, D.; Pérez, R.y.A.L. Métodos de Estimación de la Biomasa Potencial. 2012. Available online: https://www.researchgate.net/publication/280086387_Metodos_de_estimacion_de_la_biomasa_potencial (accessed on 20 February 2022).
  29. Pohekar, S.D.; Ramachandran, M. Application of multi-criteria decision making to sustainable energy planning—A review. Renew. Sustain. Energy Rev. 2004, 8, 365–381. [Google Scholar] [CrossRef]
  30. González, C.M.R. Microalgae-Based Biofuels and Bioproducts: From Feedstock Cultivation to End-Products; Woodhead Publishing Elsevier Ltd.: Amsterdam, The Netherlands, 2017. [Google Scholar]
  31. Bolivar, R.A.; Mostany, J.; del Carmen García, M. Petróleo versus energías alternas. Dilema futuro. Interciencia 2006, 31, 704–711. [Google Scholar]
  32. Demirbaş, A. Biomass resource facilities and biomass conversion processing for fuels and chemicals. Energy Convers. Manag. 2001, 42, 1357–1378. [Google Scholar] [CrossRef]
  33. Clery, D.S.; Vaughan, N.E.; Forster, J.; Lorenzoni, I.; Gough, C.A.; Chilvers, J. Bringing greenhouse gas removal down to earth: Stakeholder supply chain appraisals reveal complex challenges. Glob. Environ. Chang. 2021, 71, 102369. [Google Scholar] [CrossRef]
  34. Titov, A.; Kövér, G.; Tóth, K.; Gelencsér, G.; Kovács, B. Acceptance and Potential of Renew-able Energy Sources Based on Biomass in Rural Areas of Hungary. Sustainability 2021, 13, 2294. [Google Scholar] [CrossRef]
  35. Lerma Arce, V. Planificación, Logística y Valorización de Biomasa Forestal Residual en la Provincia de Valencia; Universitat Politècnica de València: Valencia, Spain, 2015. [Google Scholar]
  36. Mariano, F. Meta de producción de los 300 MW en biomasa para el año 2030. In Bioenergía Dominicana; Revista del Proyecto de Bioelectricidad Industrial: Santo Domingo, Dominican Republic, 2018. [Google Scholar]
  37. IRENA. Prospectivas de Energías Renovables: República Dominicana, REmap 2030. 2016. Available online: https://www.cne.gob.do/wp-content/uploads/2018/01/2820172920ESP20REmap20RD202030.pdf (accessed on 20 February 2022).
  38. Espinasa, R.; Balza, L.; Hinestrosa, C.; Sucre, C. Dossier energético: República Dominicana; Banco Interamericano de Desarrollo: Washington, DC, USA, 2013. [Google Scholar]
  39. Frías, A.; Checo, H.; Reyes, J. Estudio de la Producción Actual y Potencial de Biomasa en República Dominicana y su Plan de Aprovechamiento para la Generación de Energía; Comisión Nacional de Energía: Santo Domingo, Dominican Republic, 2018. [Google Scholar]
  40. Martínez, C.y.C.C. Mix de Generación en el Sistema Electrico Español en el Horizonte 2030; Foro de la Industria Nuclear Española: Madrid, Spain, 2007. [Google Scholar]
  41. Carbajo, J. La Integración de las Energías Renovables en el Sistema Eléctrico; Fundación Alternativas: Madrid, Spain, 2012. [Google Scholar] [CrossRef]
  42. Schweers, W.; Bai, Z.; Campbell, E.; Hennenberg, K.; Fritsche, U.; Mang, H.-P.; Lucas, M.; Li, Z.; Scanlon, A.; Chen, H.; et al. Identification of potential areas for biomass production in China: Discussion of a recent approach and future challenges. Biomass Bioenergy 2011, 35, 2268–2279. [Google Scholar] [CrossRef]
  43. Ochs, A.; Konold, M.; Lucky, M.; Musolino, E.; Weber, M. Hoja de Ruta para un Sistema de Energía Sostenible: Aprovechamiento de los Recursos de Energía Sostenible de la República Dominicana; Worldwatch Institute: Washington, DC, USA, 2015. [Google Scholar] [CrossRef]
  44. Zahraee, S.M.; Shiwakoti, N.; Stasinopoulos, P. Biomass supply chain environmental and socio-economic analysis: 40-Years comprehensive review of methods, decision issues, sustainability challenges, and the way forward. Biomass Bioenergy 2020, 142, 105777. [Google Scholar] [CrossRef]
  45. Johansson, V.; Lehtveer, M.; Göransson, L. Biomass in the electricity system: A complement to variable renewables or a source of negative emissions? Energy 2019, 168, 532–541. [Google Scholar] [CrossRef]
  46. Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew. Sustain. Energy Rev. 2009, 13, 2263–2278. [Google Scholar] [CrossRef]
  47. Droege, P. Contributor Biographies. In Urban Energy Transition; Elsevier Ltd.: Amsterdam, The Netherlands, 2018. [Google Scholar]
  48. Domínguez, J. Los Sistemas de Información Geográfica en la Planificación e Integración de Energías Renovables; CIEMAT: Madrid, Spain, 2002. [Google Scholar]
  49. Velázquez, B. Situación de los sistemas de aprovechamiento de los residuos forestales para su utilización energética. Ecosistemas Rev. Científica Ecol. Medio Ambiente 2006, 15, 77–86. [Google Scholar]
  50. Wu, J.; Zhang, J.; Yi, W.; Cai, H.; Li, Y.; Su, Z. Agri-biomass supply chain optimization in north China: Model development and application. Energy 2022, 239, 122374. [Google Scholar] [CrossRef]
  51. De la Paz, C.; Domínguez, J.y.P.M. Metodología SIG para la Localización de Centrales de Biomasa mediante Nota Multicriterio y Análisis de Redes. In Modelos de Localización-Asignación para el Aprovechamiento de Biomasa Forestal; CIEMAT: Madrid, Spain, 2013. [Google Scholar]
  52. Long, H.; Li, X.; Wang, H.; Jia, J. Biomass resources and their bioenergy potential estimation: A review. Renew. Sustain. Energy Rev. 2013, 26, 344–352. [Google Scholar] [CrossRef]
  53. Guo, C.; Hu, H.; Wang, S.; Rodriguez, L.F.; Ting, K.; Lin, T. Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply. Renew. Energy 2022, 186, 378–393. [Google Scholar] [CrossRef]
  54. Nunes, L.J.R.; Causer, T.P.; Ciolkosz, D. Biomass for energy: A review on supply chain management models. Renew. Sustain. Energy Rev. 2020, 120, 109658. [Google Scholar] [CrossRef]
  55. Ahamed, T.; Tian, L.; Zhang, Y.; Ting, K.C. A review of remote sensing methods for biomass feedstock production. Biomass Bioenergy 2011, 35, 2455–2469. [Google Scholar] [CrossRef]
  56. Amirante, R.; Bruno, S.; Distasoa, E.; La Scalab, M.T.P. A biomass small-scale externally fired combined cycle plant for heat and power generation in rural communities. Renew. Energy Focus 2019, 28, 26–46. [Google Scholar] [CrossRef]
  57. Navarro Cerrillo, R.M.; Palacios Rodríguez, G.; Clavero Rumbao, I.; Lara, M.Á.; Bonet, F.J.; Mesas-Carrascosa, F.-J. Modeling major rural land-use changes using the GIS-based cellular automata metronamica model: The case of Andalusia (Southern Spain). ISPRS Int. J. Geo-Inf. 2020, 9, 458. [Google Scholar] [CrossRef]
  58. Gnansounou, E.; Pandey, A. Life-Cycle Assessment of Biorefineries; Elsevier Ltd.: Amsterdam, The Netherlands, 2017. [Google Scholar]
  59. CNE. Reglamento de la Ley 57-07; Comisión Nacional de Energía: Madrid, Spain, 2015. [Google Scholar]
  60. Özyuguran, A.Y.S. Prediction of Calorific Value of Biomass from Proximate Analysis. Energy Procedia 2017, 107, 130–136. [Google Scholar] [CrossRef]
  61. Clauser, N.M.; González, G.; Mendieta, C.M.; Kruyeniski, J.; Area, M.C.; Vallejos, M.E. Biomass waste as sustainable raw material for energy and fuels. Sustainability 2021, 13, 794. [Google Scholar] [CrossRef]
  62. Broek, R.; Faaij, A.; Wijk, A. Biomass combustion power generation technologies. Biomass Bioenergy 1996, 11, 271–281. [Google Scholar]
  63. Abascal, R. Estudio de la Obtención de Bioetanol a Partir de Diferentes Tipos de Biomasa Lignocelulósica. Matriz de Reacciones y Optimización; Trabajo de fin de grado; Universidad de Cantabria, Escuela Politécnica de Ingeniería de Minas y Energía: Cantabria, Spain, 2017. [Google Scholar]
  64. Ayala-Mendivil, N.; Sandoval, G. Bioenergía a partir de residuos forestales y de madera. Madera Bosques 2018, 24. [Google Scholar] [CrossRef]
  65. Mboumboue, E.N.D. Biomass resources assessment and bioenergy generation for a clean and sustainable development in Cameroon. Biomass Bioenergy 2018, 118, 16–23. [Google Scholar] [CrossRef]
  66. Wielgosinski, G.; Łechtanska, P.N.O. Emission of some pollutants from biomass combustion in comparison to hard coal combustión. J. Energy Inst. 2016, 90, 787–796. [Google Scholar] [CrossRef]
  67. Rico, J. La Ciencia Irrumpe Contra la Biomasa Forestal para Energía, pero También en su Defensa. Energías Renovables. 2018. Available online: https://www.energias-renovables.com/bioenergia/la-ciencia-irrumpe-contra-la-biomasa-forestal-20181029 (accessed on 20 February 2022).
  68. Malico, I.; Nepomuceno, R.; Gonçalves, A.S.A. Current status and future perspectives for energy production from solid biomass in the European industry. Renew. Sustain. Energy Rev. 2019, 112, 960–977. [Google Scholar] [CrossRef]
  69. He, J.; Liu, Y.; Lin, B. Should China support the development of biomass power generation? Energy 2018, 163, 416–425. [Google Scholar] [CrossRef]
  70. Demirbas, A. Potential applications of renewable energy sources, biomass combustion problems in boiler power systems and combustión related environmental issues. Prog. Energy Combust. Sci. 2005, 31, 171–192. [Google Scholar] [CrossRef]
  71. Dupuis, D.; Grim, G.; Nelson, E.; Tan, E.; Ruddy, D.; Hernandez, S.; Westoverb, T.; Hensley, J.; Carpenter, D. High-Octane Gasoline from Biomass: Experimental, Economic, and Environmental Assessment. Appl. Energy 2019, 241, 23–33. [Google Scholar] [CrossRef]
  72. Gambarotta, A.; Morini, M.; Zubani, A. A Model for the Prediction of Pollutant Species Production in the Biomass Gasification Process. Energy Procedia 2017, 105, 700–705. [Google Scholar] [CrossRef]
  73. Mendoza, C.; Alves, E.; Oliveira, A.; Borges, F.; Ribas, L.; Vakkilainene, E.C.M. Characterization of residual biomasses from the coffee production chain and assessment the potential for energy purposes. Biomass Bioenergy 2019, 120, 68–76. [Google Scholar] [CrossRef]
  74. Mohd Idris, M.N.; Hashim, H.; Razak, N.H. Spatial optimisation of oil palm biomass co-firing for emissions reduction in coal-fired power plant. J. Clean. Prod. 2018, 172, 3428–3447. [Google Scholar] [CrossRef]
  75. Mustafa, B.G.; Kiah, M.H.M.; Irshad, A.; Andrews, G.E.; Phylaktou, H.N.; Li, H.; Gibbs, B.M. Rich biomass combustion: Gaseous and particle number emissions. Fuel 2019, 248, 221–231. [Google Scholar] [CrossRef]
  76. Pfau, S.; Hanssen, S.; Straatsma, M.; Koopman, R.; Leuven, R.H.M. Life cycle greenhouse gas benefits or burdens of residual biomass from landscape management. J. Clean. Prod. 2019, 220, 698–706. [Google Scholar] [CrossRef]
  77. Rahman, A.; Rasul, M.K.; Sharma, S. Recent development on the uses of alternative fuels in cement manufacturing process. Fuel 2014, 145, 84–99. [Google Scholar] [CrossRef]
  78. Zhou, Y.; Luo, B.; Li, J.; Hao, Y.; Yang, W.; Shi, F.; Chen, Y.; Simayi, M.; Xie, S. Characteristics of six criteria air pollutants before, during, and after a severe air pollution episode caused by biomass burning in the southern Sichuan Basin, China. Atmos. Environ. 2019, 215, 116840. [Google Scholar] [CrossRef]
  79. Germán, B. Gestión de Cadenas de Suministro en el Aprovechamiento de Biomasa; El Telégrafo: Guayaquil, Ecuador, 2019. [Google Scholar]
  80. Duc, D.N.; Meejaroen, P.; Nananukul, N. Multi-objective models for biomass supply chain planning with economic and carbon footprint consideration. Energy Rep. 2021, 7, 6833–6843. [Google Scholar] [CrossRef]
  81. Moretti, L.; Milani, M.; Lozza, G.G.; Manzolini, G. A detailed MILP formulation for the optimal design of advanced biofuel supply chains. Renew. Energy 2021, 171, 159–175. [Google Scholar] [CrossRef]
  82. O’Neill, E.G.; Maravelias, C.T. Towards integrated landscape design and biofuel supply chain optimization. Curr. Opin. Chem. Eng. 2021, 31, 100666. [Google Scholar] [CrossRef]
  83. Ng, W.P.Q.; How, B.S.; Lim, C.H.; Ngan, S.L.; Lam, H.L. Chapter 20—Biomass supply chain synthesis and optimization. In Value-Chain of Biofuels; Yusup, S., Rashidi, N.A., Eds.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 445–479. [Google Scholar]
  84. Cooper, N.; Panteli, A.; Shah, N. Linear estimators of biomass yield maps for improved biomass supply chain Optimisation. Appl. Energy 2019, 253, 113526. [Google Scholar] [CrossRef]
  85. Eliasson, L.; Anerud, E.; Grönlund, Ö.V.H.H. Managing moisture content during storage of logging residues at landings e Effects of coverage strategies. Renew. Energy 2020, 145, 2510–2515. [Google Scholar] [CrossRef]
  86. Acuna, M.; Sessions, J.; Zamora, R.; Boston, K.; Brown, M.; Reza, M. Methods to Manage and Optimize Forest Biomass Supply Chains: A Review. Curr. For. Rep. 2016, 5, 124–141. [Google Scholar] [CrossRef]
  87. Akhtari, S.; Sowlati1, T.; Day, K. Optimal flow of regional forest biomass to a district heating system. Int. J. Energy Res. 2014, 38, 954–964. [Google Scholar] [CrossRef]
  88. Cintas, O.; Berndes, G.; Englund, O.; Johnsson, F. Geospatial supply-demand modeling of lignocellulosic biomass for electricity and biofuels in the European Union. Biomass Bioenergy 2021, 144, 105870. [Google Scholar] [CrossRef]
  89. Freer, M.; Gough, C.; Welfle, A.; Lea-Langton, A. Carbon optimal bioenergy with carbon capture and storage supply chain modelling: How far is too far? Sustain. Energy Technol. Assess. 2021, 47, 101406. [Google Scholar] [CrossRef]
  90. Gautam, S.; LeBel, L.; Carle, M. Supply chain model to assess the feasibility of incorporating a terminal between forests and biorefineries. Appl. Energy 2017, 198, 377–384. [Google Scholar] [CrossRef]
  91. Gunnarsson, H.; Rönnqvist, M.; Lundgren, J. Supply chain modelling of forest fuel. Eur. J. Oper. Res. 2004, 158, 103–123. [Google Scholar] [CrossRef]
  92. Han, S.; Murphy, G. Solving a woody biomass truck scheduling problema for a transport company in Western Oregon, USA. Biomass Bioenergy 2012, 44, 47–55. [Google Scholar] [CrossRef]
  93. Jeong, J.R.-G.A. Optimizing the location of a biomass plant with a fuzzy-DEcision-MAking Trial and Evaluation Laboratory (F-DEMATEL) and multi-criteria spatial decision assessment for renewable energy management and long-term sustainability. J. Clean. Prod. 2018, 182, 509–520. [Google Scholar] [CrossRef]
  94. Kanzian, C.; Kühmaier, M.; Zazgornik, J.; Stampfer, K. Design of forest energy supply networks using multi-objective optimization. Biomass Bioenergy 2013, 58, 294–302. [Google Scholar] [CrossRef]
  95. Marufuzzaman, M.D.S. Managing congestion in supply chains via dynamic freight routing: An application in the biomass supply chain. Transp. Res. 2017, 99, 54–76. [Google Scholar] [CrossRef]
  96. Memişoğlu, G.Ü.H. Integrated Bioenergy Supply Chain Network Planning Problem. Transp. Sci. 2015, 50, 35–56. [Google Scholar] [CrossRef]
  97. Miret, C.; Chazara, P.; Montastruc, L.; Negny, S.; Domenech, S. Design of bioethanol green supply chain: Comparison between first and second generation biomass concerning economic, environmental and social criteria. Comput. Chem. Eng. 2015, 85, 16–35. [Google Scholar] [CrossRef] [Green Version]
  98. Zahraee, S.M.; Golroudbary, S.R.; Shiwakoti, N.; Stasinopoulos, P.; Kraslawski, A. Economic and environmental assessment of biomass supply chain for design of transportation modes: Strategic and tactical decisions point of view. Procedia CIRP 2021, 100, 780–785. [Google Scholar] [CrossRef]
  99. Ramírez, L.; Stoeglehner, G. Spatiotemporal modelling for integrated spatial and energy planning. Energy Sustain. Soc. 2018, 8, 32. [Google Scholar] [CrossRef]
  100. Rex, N.M.C. Design of Cellulosic Ethanol Supply Chains with Regional Depots. Ind. Eng. Chem. Res. 2016, 55, 3420–3432. [Google Scholar]
  101. Roni, M.; Eksioglu, S.; Cafferty, K.J.J. A Multi-Objective, Hub- and- Spoke Model to Design and Manage Biofuel Supply Chains; Springer Science+Business Media: New York, NY, USA, 2016. [Google Scholar]
  102. Santibañez-Aguilar, J.; Lozano-García, D.; Lozano, F.; Flores-Tlacuahuac, A. Sequential Use of Geographic Information System and Mathematical Programming for Optimal Planning for Energy Production Systems from Residual Biomass. Ind. Eng. Chem. Res. 2019, 58, 15818–15837. [Google Scholar] [CrossRef]
  103. Sakari, T.H.A. Management planning method for sustainable Energy production from forest biomass: Development of an optimization system and case study for a finnish energy plant. Environ. Eng. Manag. J. 2018, 17, 685–696. [Google Scholar]
  104. Shabani, N.S.T. A hybrid multi-stage stochastic programming-robust optimization model for maximizing the supply chain of a forest-based biomass power plant considering uncertainties. J. Clean. Prod. 2016, 112, 3285–3293. [Google Scholar] [CrossRef] [Green Version]
  105. Shabani, N.S.T. A mixed integer non-linear programming model for tactical value chain optimization of a wood biomass power plant. Appl. Energy 2013, 104, 353–361. [Google Scholar] [CrossRef]
  106. Valenti, F.; Liao, W.P.S. A GIS-based spatial index of feedstock- mixture availability for anaerobic co-digestion of mediterranean by-products and agricultural residue. Biofuels Bioprod. Biorefin. 2018, 12, 362–378. [Google Scholar] [CrossRef]
  107. Venturini, G.; Pizarro, A.; Münster, M. How to maximise the value of residual biomass resources: The case of Straw in Denmark. Appl. Energy 2019, 50, 369–388. [Google Scholar] [CrossRef]
  108. Bonato, S.V.; Augusto de Jesus Pacheco, D.; Schwengber ten Caten, C.; Caro, D. The missing link of circularity in small breweries’ value chains: Unveiling strategies for waste management and biomass valorization. J. Clean. Prod. 2022, 336, 130275. [Google Scholar] [CrossRef]
  109. Woo, H.; Acuna, M.; Moroni, M.; Taskhiri, M.T.P. Optimizing the Location of Biomass Energy Facilities by Integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems. Forests 2018, 9, 585. [Google Scholar] [CrossRef] [Green Version]
  110. Ye, Q.; Bruckner, M.; Wang, R.; Schyns, J.F.; Zhuo, L.; Yang, L.; Su, H.; Krol, M.S. A hybrid multi-regional input-output model of China: Integrating the physical agricultural biomass and food system into the monetary supply chain. Resour. Conserv. Recycl. 2022, 177, 105981. [Google Scholar] [CrossRef]
  111. Zamar, D.; Gopaluni, B.S.S. Optimization of sawmill residues collection for bioenergy production. Appl. Energy 2017, 202, 487–495. [Google Scholar] [CrossRef]
  112. Zhang, F.; Johnson, D.W.J. Integrating multimodal transport into forest- delivered biofuel supply chain design. Renew. Energy 2016, 93, 58–67. [Google Scholar] [CrossRef]
  113. Mena, G. La Ingeniería Industrial, clave en la optimización de la cadena de valor de la biomasa. In Bioenergía Dominicana; BioElectricidad Industrial: Santo Domingo, Dominican Republic, 2018; pp. 20–21. [Google Scholar]
  114. García-Velásquez, C.; Leduc, S.; van der Meer, Y. Design of biobased supply chains on a life cycle basis: A bi-objective optimization model and a case study of biobased polyethylene terephthalate (PET). Sustain. Prod. Consum. 2022, 30, 706–719. [Google Scholar] [CrossRef]
  115. Núñez, I.y.V.V. Evolución de Costos ERNC—Costos Biomasa. Mercados Eléctricos-IEE3372s. 2012. Available online: https://hrudnick.sitios.ing.uc.cl/alumno12/costosernc/Inicio.html (accessed on 20 February 2022).
  116. Saygin, D.; Gielen, D.; Draeck, M.; Worrel, E.P.M. Assessment of the technical and economic potentials of biomass use for the production of steam, chemicals and polymers. Renew. Sustain. Energy Rev. 2014, 40, 1153–1167. [Google Scholar] [CrossRef]
  117. Jackson, R.; Ferreira, A.E.E. Woody biomass processing: Potential economic impacts on rural regions. Energy Policy 2018, 115, 66–77. [Google Scholar] [CrossRef] [Green Version]
  118. Wang, Y.; Li, G.; Liu, Z.; Cui, P.; Zhu, Z.Y.S. Techno-economic analysis of biomass-to-hydrogen process in comparison with coal-to-hydrogen process. Energy 2019, 185, 1063–1075. [Google Scholar] [CrossRef]
  119. AlNouss, A.; McKay, G.A.-A.T. A comparison of steam and oxygen fed biomass gasification through a techno-economic-environmental study. Energy Convers. Manag. 2020, 208, 112612. [Google Scholar] [CrossRef]
  120. AlNouss, A.; McKay, G.A.-A.T. Enhancing waste to hydrogen production through biomass feedstock blending: A techno-economic-environmental evaluation. Appl. Energy 2020, 266, 114885. [Google Scholar] [CrossRef]
  121. Battuvshin, B.; Matsuoka, Y.; Shirasawa, H.; Toyama, K.; Hayashi, U.A.K. Supply potential and annual availability of timber and forest biomass resources for energy considering inter-prefectural trade in Japan. Land Use Policy 2020, 97, 104780. [Google Scholar] [CrossRef]
  122. Kalina, J. Options for using solid oxide fuel cell technology in complex integrated biomass gasification cogeneration plants. Biomass Bioenergy 2019, 122, 400–413. [Google Scholar] [CrossRef]
  123. Kavitha, S.; Schikaran, M.; Yukesh, R.; Gunasekaran, M.; Kumar, G.; Banu, R. Nanoparticle induced biological disintegration: A new phase separated pretreatment strategy on microalgal biomass for profitable biomethane recovery. Bioresour. Technol. 2019, 289, 121624. [Google Scholar] [CrossRef]
  124. Kreutz, T.; Larson, E.; Elsido, C.; Martelli, E.; Greig, R.W.R. Techno-economic prospects for producing Fischer-Tropsch jet fuel and electricity from lignite and woody biomass with CO2 capture for EOR. Appl. Energy 2020, 279, 115841. [Google Scholar] [CrossRef]
  125. Matłok, N.G.J. Assessment of cost and energy effectiveness of modified technologies for production of young fruit trees, taking into account the use of waste biomass for energy and soil amendment related purposes. Energy 2020, 190, 116428. [Google Scholar] [CrossRef]
  126. Patel, B.; Patel, A.; Gami, B.P.P. Energy balance, GHG emission and economy for cultivation of high biomass verities of bamboo, sorghum and pearl millet as energy crops at marginal ecologies of Gujarat state in India. Renew. Energy 2020, 148, 816–823. [Google Scholar] [CrossRef]
  127. Rajesh, J.; Yukesh, R.; Kavitha, S.; Ashikvivek, A.; Bhosale, R.; Kumar, G. Cost effective biomethanation via surfactant coupled ultrasonic lique- faction of mixed microalgal biomass harvested from open raceway pond. Bioresour. Technol. 2020, 304, 123021. [Google Scholar] [CrossRef] [PubMed]
  128. Sacit, M.; Mazzeo, D.; Matera, N.; Wen, J.; Nathwani, J.H.Z. Simulation and modeling of a combined biomass gasification-solar photovoltaic hydrogen production system for methanol synthesis via carbon dioxide hydrogenation. Energy Convers. Manag. 2020, 219, 113045. [Google Scholar]
  129. Sandar, S.; Tibebu, D.; Shromova, O.; Kuittinen, S.; Turunen, O.P.A. Lake bottom biomass as a potential source for the biorefining industry. Bioresour. Technol. Rep. 2019, 7, 100282. [Google Scholar] [CrossRef]
  130. Schnorf, V.; Trutnevyte, E.; Bowman, G.; Burg, V. Biomass transport for energy: Cost, energy and CO2 performance of forest wood and manure transport chains in Switzerland. J. Clean. Prod. 2021, 293, 125971. [Google Scholar] [CrossRef]
  131. Steinbrück, J.; Tavakkol, S.; Francis, G.B.H. Jatropha—Potential of biomass steam processing to convert crop residues to bio-coal and thus triple the marketable energy output per unit plantation area. Ind. Crops Prod. 2019, 136, 59–65. [Google Scholar] [CrossRef]
  132. Sui, Y.; Jiang, Y.; Moretti, M.; Vlaeminck, S. Harvesting time and biomass composition affect the economics of microalgae production. J. Clean. Prod. 2020, 259, 120782. [Google Scholar] [CrossRef]
  133. Heidenreich, S.U.P. New concepts in biomass gasification. Prog. Energy Combust. Sci. 2015, 46, 72–95. [Google Scholar] [CrossRef]
  134. Ronia, M.; Chowdhuryb, S.; Mamun, S.; Marufuzzaman, M.; Leind, W.J.S. Biomass co-firing technology with policies, challenges, and opportunities: A global review. Renew. Sustain. Energy Rev. 2017, 78, 1089–1101. [Google Scholar] [CrossRef]
  135. Sharifzadeh, M.; Sadeqzadeh, M.; Guo, M.; Borhani, T.; Konda, M.; Cortada, M.; Wang, L.; Hallett, J.S.N. The multi-scale challenges of biomass fast pyrolysis and bio-oil upgrading: Review of the state of art and future researchdirections. Prog. Energy Combust. Sci. 2019, 71, 1–80. [Google Scholar] [CrossRef]
  136. Guo, W.; Cheng, J.; Liu, S.; Feng, L.; Su, L.L.Y. A novel porous nickel-foam filled CO2 absorptive photobioreactor system to promote CO2 conversion by microalgal biomass. Sci. Total Environ. 2020, 713, 136593. [Google Scholar] [CrossRef]
  137. Feng, J.; Tong, L.; Ma, C.; Xu, Y.J.J.; Yang, Z.P.H. Directional and integrated conversion of whole components in biomass for levulinates and phenolics with biphasic system. Bioresour. Technol. 2020, 315, 123776. [Google Scholar] [CrossRef] [PubMed]
  138. Goffé, J.y.F.J. Stoichiometry impact on the optimum efficiency of biomass conversion to biofuels. Energy 2019, 170, 438–458. [Google Scholar] [CrossRef]
  139. Guo, X.; An, Y.; Chai, C.; Sang, J.; Jiang, L.; Lu, F.; Dai, Y.; Liu, F. Construction of the R17L mutant of MtC1LPMO for improved lignocellulosic biomass conversion by rational point mutation and investigation of the mechanism by molecular dynamics simulations. Bioresour. Technol. 2020, 317, 124024. [Google Scholar] [CrossRef]
  140. Ha, G.; El-Dalatony, M.; Kurade, M.; Salama, E.; Basak, B.; Kang, D.; Roh, H.; Lim, H.J.B. Energy-efficient pretreatments for the enhanced conversion of microalgal biomass to biofuels. Bioresour. Technol. 2020, 309, 123333. [Google Scholar] [CrossRef] [PubMed]
  141. He, X. A Novel Hybrid Digestion-Gasification Process Integrated with Membranes for Efficient Conversion of Biomass to Bio-alcohols. Green Energy Environ. 2020, 6, 15–21. [Google Scholar] [CrossRef]
  142. Deralia, P.K.; Sonker, A.K.; Lund, A.; Larsson, A.; Strom, A.; Westman, G. Side chains affect the melt processing and stretchability of arabinoxylan biomass-based thermoplastic films. Chemosphere 2022, 294, 133618. [Google Scholar] [CrossRef]
  143. Li, J.; Tao, J.; Yan, B.; Cheng, K.; Chen, G.H.J. Microwave reforming with char-supported Nickel-Cerium catalysts: A potential approach for thorough conversion of biomass tar model compound. Appl. Energy 2020, 261, 114375. [Google Scholar] [CrossRef]
  144. Paul, T.; Sinharoy, A.; Pakshirajan, K.; Pugazhenthi, G. Lipid-rich bacterial biomass production using refinery wastewater in a bubble column bioreactor for bio-oil conversion by hydrothermal liquefaction. J. Water Process Eng. 2020, 37, 101462. [Google Scholar] [CrossRef]
  145. Puthiyamadam, A.; Prasannakumari, V.; Kumar, K.; Mathew, A.; Kumar, J.; Yenumala, R.; Bhaskar, T.; Beevi, S.; Sahoo, D.S.R. Evaluation of a wet processing strategy for mixed phumdi biomass conversion to bioethanol. Bioresour. Technol. 2019, 289, 121633. [Google Scholar] [CrossRef]
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