ABSTRACT
Biomass has been one of the main energy providers in rural areas for long time and creates significant socio-economic developments, environmental benefits, and sustainability. The energy crop models, mathematical models, and process-based models have high potential to simulate the sustainable production and yield of energy crops, crop rotations, conversion of biomass into energy and biomaterials in a biorefinery system, economics of bioenergy supply chain logistics, environmental effects of bioenergy system, and life cycle assessment. These models should include more accurate field level data and estimate uncertainty to simulate the biomass yield of energy crops. Up to the present time, over 20 models are developed by different studies to simulate the yields of different energy crops. Different studies modeled the biomass production potential of Eucalyptus camaldulensis, switchgrass, miscanthus, maize, poplar, willow, and sugarcane that have various yield potentials and energy conversion efficiencies. There are three types of mechanistic plant-growth models, such as radiation model, water-controlled crop model, and integrated model, developed based on different principles or approaches. Biomass supply chain can supply the biomass resources efficiently for biorefinery industries. The supply chain model and farm-scale economic models can explain the annual profit and uptake of energy crops compared to traditional food crops using the theory of supply chain economics. There are five categories of biomass supply chain modeling, such as harvesting of biomass, biomass pre-treatment, biomass storage, biomass transport, and biomass energy conversion. The biomass gasification is a thermo-chemical conversion process that produces both heat and some intermediate chemicals, such as syngas (CO + H2) for commercial use to produce combined heat and power (CHP), drop-in diesel by catalytic Fischer−Tropsch process, electricity, synthetic natural gas, and methanol. The computational modeling tools can identify the optimal states of a biomass conversion reactor without the time-consuming and expensive experimentation. The mathematical models of gasification process are classified into (i) thermodynamic equilibrium models, (ii) kinetic models, and (iii) artificial neural network models. There are some benefits and problems associated with equilibrium models or kinetic models or combination of both. Computational Fluid Dynamics model can simulate the performance of a gasifier reactor including the positive benefits of both models. Artificial Neural Network modeling can also successfully simulate the performance of gasifiers.
