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Microalgae have received a great deal of attention among researchers in recent decades in the production of sustainable bioenergy due to the limitations of second-generation biofuels. However, scalability and economics are the two key challenges that need to be overcome for sustainable biofuel production at field level, and mathematical modelling can be utilized as an effective tool to evaluate the influencing factors. This article focuses on the mathematical modelling of microalgal growth and carbon dioxide sequestration potential of a fixed photobioreactor (PBR) at 25 inclination facing south and a two-axis trackable PBR in Odisha, India. The total geographic area of Odisha has been represented in 1195 spatial sites, each site representing around 130 sq. km of the real scale dimensions approximately. The model incorporates site-specific data of solar radiation, climatic conditions and PBR configurations to derive the bioenergy content of microalgal biomass by photon energy balance. The effect of photoinhibition was also studied, and the outputs from the mathematical modelling, such as daily microalgal lipid production and carbon dioxide sequestration potential were plotted for the whole of Odisha using QGIS software. The net microalgal biomass production rate drastically reduced to around 30% and 40% due to the effect of photoinhibition in the case of fixed and trackable PBR systems respectively. The outcome of the present study could influence the policy-makers for selecting suitable sites for the implementation of microalgal-based biofuel production facility.

The decision to install a PV plant depends on three major factors: the climatic and environment conditions of the location, the viability of commercial operations, and the government policies. Economic feasibility of a PV system in the energy market is largely driven by the cost of technology, the cost of installation, and the yield of the plant. Considering uncertain nature of geographical parameters (solar radiation, temperature, dust accumulation, etc.), development of a reliable model to predict the energy output of a plant-to-be installed becomes essential. The model ensures the long-term performance criteria of the PV system. The proposed model considers a case study of Odisha by taking only two meteorological variables collected from 1195 locations: total annual incident global radiation on the PV module and annual average air temperature. The developed model is independent of longitude and latitude, elevation, and other environment conditions. Model is validated using the data collected from SN Mohanty solar power plant situated at Cuttack. The paper focuses on simplification at every stage of the development while validating the preciseness of the model.

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