The study was implemented through mixed research methodologies as shown in Fig. 2, with both qualitative approaches represented by interviews conducted with members of the local population and representatives from the rice companies and quantitative approaches represented by the gathering and analysis of data for the SAM simulator to design a clean energy production plant. The mixed methodology contemplates the development of four phases, which are described below:
Determination of physicochemical properties
The collected rice husk samples were analyzed to determine the following physicochemical properties: nitrogen content (%), organic carbon (%), silica (%), moisture determination (%), ash (%), presence of carbonates (%), and pH. Table 1 specifies the method or analytical technique used for each property. The various laboratory tests were performed by Chemilab, a laboratory accredited by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM).
Energy valuation model
During this phase, the System Advisor Model (SAM) simulator was used to validate and simulate collected information. The SAM simulator is a techno-economic software designed by the National Renewable Energy Laboratory that facilitates decision-making in renewable energy projects (Home - System Advisor Model - SAM., n.d.).
The SAM simulator presents a biomass combustion component that allows for the modeling of bioenergy plants that use crop residues and wood as raw material, facilitating the modeling of biomass combustion energy systems and generating performance metrics such as heat rate, thermal efficiency, and factor of capacity. Additionally, the SAM simulator can evaluate the financial viability of a project through financial indicators such as the leveling cost of energy (LCOE), the net present value (NPV), and the collection period (Biomass Combustion - System Advisor Model - SAM., n.d.). The SAM simulator was used in this project to analyze an energetic model of the rice husk by taking into account the physicochemical properties obtained as a result of the previously described laboratory tests. The following inputs are needed for the analysis: the location, system design parameters, and biomass properties through which the estimated capacity of a biomass power plant is determined, as well as its capacity factor, rate of heat, and power produced (Abdelhady et al., 2018).
The internal calculations in the SAM bioenergy model use a different time scale for each of its calculated parameters. In this way, moisture content is calculated on by months whereas ambient temperature is calculated by hours and then averaged monthly due to the fact that the efficiency of the boiler and the turbines varies with respect to this variable (Jorgenson et al., 2011).
Bioenergy plant perception surveys
Given the importance of social aspects for the development of this project, an evaluation of the communities surrounding the rice zone of Tolima was performed, with the objective of identifying the degree of connection of the inhabitants with the industry, rice farm, knowledge about local agricultural activity, and finally degree of interest in the development of a rice husk processing plant (Ferrari et al., 2022).
Table 1
Physicochemical parameters of rice husk
Parameter | Result | Method – analytical technique |
Total nitrogen | 0.74% | Semi-Micro Kjeldahl |
Organic carbon | 38.04% | IGAG |
Silica | 18.39% | SM 4500 Si D |
Carbonates | < 0.01% | IGAG – Potentiometric titration |
Ashes | 19.4% | Calcination a 550°C |
Natural humidity | 7.68% | ASTM D2216-98 – Gravimetric |
pH | 6.41 | NTC 5264 – Electrometry |
Based on the above, a survey was conducted aimed at various social groups present in the study area that have a direct or indirect relationship with the rice industry in Tolima. The survey presented two main types of variables: discriminant and evaluative. Discriminant variables have the objective of demographically profiling people by gender, age, academic level, relationship with the rice-producing industry, and social group. Evaluative variables serve to record the assessment of those surveyed towards the implementation of a rice husk transformation plant in search of a sustainable energy alternative (Quevedo, 2011).
In order to perform a correct statistical analysis of the information collected through the survey, a data analysis methodology was used which sought to integrate statistical techniques such as measures of central tendency and dispersion, and computer science for data analysis.
Triple Bottom Line (TPL)
The Triple Bottom Line (TPL) was a methodology designed by John Elkington in 1998 to assess sustainability and measure performance beyond traditional measures of profit, return on investment, and shareholder value, so that environmental and social dimensions are included (García López, 2015). The idea of the TPL is to satisfy the demands of the stakeholders in a project by understanding the economic, social, and environmental results. The sustainability of the project is evaluated by measuring impact, with emphasis on environmental capillary, social capital, profitability, and social responsibility of the various interest groups involved in the project (Rodríguez Guerra & Ríos Osorio, 2016):
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Economic factor: The economic variables evaluated in the project are related to technical-financial aspects such as project financing, operating and non-operating expenses, and local economic development.
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Social factor: During the development of a project, it is important to evaluate the impact on local communities, such as, in the case of this project, Colombian regulations for agricultural activities. These interests may influence the social acceptance of the project.
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Environmental factor: Environmental variables consist of those characteristics that can be evaluated to promote a healthy environment and energy security in the area.