Biorefinery plays a crucial role in the decarbonization of the current economic model, but its high investments and costs make its products less competitive. Identifying the best technological route to maximize operational synergies is crucial for its viability. This study presents a new superstructure model based on mixed integer linear programming to identify an ideal biorefinery configuration. The proposed formulation considers the selection and process scale adjustment, utility selection, and heat integration by heat cascade integration from different processes. The formulation is tested by a study where the impact of new technologies on energy efficiency and the total annualized cost of a sugarcane biorefinery is evaluated. As a result, the energy efficiency of biorefinery increased from 50.25% to 74.5% with methanol production through bagasse gasification, mainly due to its high heat availability that can be transferred to the distillery, which made it possible to shift the bagasse flow from the cogeneration to gasification process. Additionally, the production of DME yields outcomes comparable to methanol production. However, CO₂ hydrogenation negatively impacts profitability and energy efficiency due to the significant consumption and electricity cost. Nonetheless, it is advantageous for surface power density as it increases biofuel production without expanding the biomass area.

Low-carbon ammonia production is crucial for sustainable development. Brazil, a top ethanol producer, can boost competitiveness and cut emissions by integrating ammonia and ethanol production. However, optimal location and production strategy identification is challenging due to existing possibilities and uncertainties. For that, a new MILP superstructure with robust optimisation was developed and used to analyse low-carbon ammonia production integration in the ethanol industry in São Paulo state by ethanol and biomethane routes, in two different scenarios. As for the results, in scenario 1, biomethane and ethanol investments were USD 3.846 M and USD 314 M. In scenario 2, the investments were USD 316 M for biomethane and USD 259 M for ethanol. Despite the higher investment, the biomethane route results in lower hydrogen production cost (USD 1880/tonne) due to raw material prices; however, ethanol displays a higher hydrogen potential, consuming just 8% of total production against 54% of vinasse availability, which is used for biodigestion and biomethane production. In conclusion, the results suggest that the northern region of São Paulo has greater potential for ammonia facilities due to resource availability. These findings can inform and support more comprehensive studies and public incentive policies.

The development of new technologies for the production of renewable energy is fundamental to reducing greenhouse gas emissions. Therefore, the search for new energy generation methods that are environmentally responsible, socially rational, and economically viable is gaining momentum in order to mitigate carbon footprint. The aviation sector is responsible for a significant fraction of greenhouse gas emissions; for this reason, the decarbonisation of this sector must be investigated using biorefinery models. This study presents a mixed-integer linear programming (MILP) model for optimising the design and configuration of the supply chain in different states of Brazil for the production of sustainable aviation fuel (SAF) and green diesel and gasoline, using microalgae cultivated in sugarcane vinasse as the raw material. The technology of hydrothermal liquefaction was assessed in terms of its capacity to convert microalgae without need for the energy-intensive drying step. The MILP model was developed in the LINGO v.20 software using a library of physical and economic process models. We consider the selection of processes based on the object of total minimum cost, with optimal production plant scaling and regional supply chain design, including an assessment of resources and final product distribution. A case study was implemented in Brazil, considering different regions of the country and its local demands for fuels. São Paulo is the most profitable state, with a cash flow of 1071.09 and an IRR of 36.19%, far outperforming the rest. Transport emissions alone represent between 0.6 and 8.6% of emissions generated by the model. The costs of raw materials, mainly hydrogen (57%) and electricity (27%) represent the main costs evaluated in the model. The production cost (MUS$/TJ biofuel) is in the range of 0.009–0.011. Finally, changes in the cost of electricity have the greatest impact on the model.

Conventional ammonia production uses fossil-based hydrogen, resulting in high greenhouse gas emissions. Given the growing demand for sustainable solutions, it is essential to replace fossil hydrogen with renewable alternatives. This study assessed the technical, economic, and environmental viability of renewable ammonia production in Minas Gerais. To this end, an optimization model based on mixed integer linear programming (MILP) was developed and implemented in LINGO 20^® software. The model incorporated investment costs; raw materials; transportation; emissions; and indicators such as NPV, payback, and minimum sale price. Hydrogen production routes integrated into the Haber–Bosch process were analyzed: biomass gasification (GS_WGS), anaerobic digestion of vinasse (Vinasse_BD_SMR), ethanol reforming (Ethanol_ESR), and electrolysis (PEM_electrolysis). Vinasse_BD_SMR showed the lowest costs and the greatest economic viability, with a payback of just 2 years, due to the use of vinasse waste as a raw material. In contrast, the electrolysis-based route had the longest payback time (8 years), mainly due to the high cost of the electrolyzers. The substitution of conventional hydrogen made it possible to avoid 580,000 t CO₂ eq/year for a plant capacity of 200,000 t NH₃/year, which represents 13% of the Brazilian emissions from the nitrogenated fertilizer sector. It can be concluded that the viability of renewable ammonia depends on the choice of hydrogen source and logistical optimization and is essential for reducing emissions at large scale.

The worldwide transition to renewable energy systems is motivated by diminishing fossil fuel availability and the intensifying consequences of climate change. This study presents a Mixed-Integer Linear Programming (MILP) model for designing and optimising the bio-fuel and electricity supply chain in Colombia, using sugarcane as the main feedstock and integrating microalgae cultivation in vinasse. Six alternative biorefinery configurations and four microalgae conversion pathways were evaluated to inform strategic planning. The optimisation results indicate that microalgae achieve higher energy yields per unit of land than sugarcane. Ethanol production from sugarcane could meet all of Colombia’s gasoline demand, while diesel and sustainable aviation fuel derived from microalgae could supply around 9% and 16%, respectively, of the country’s consumption. Further-more, pelletised bagasse emerges as a viable alternative to replace part of the coal used in thermoelectric plants. From an economic perspective, all scenarios achieve a positive net present value, confirming their profitability. Sensitivity analysis highlights the critical factors influencing the deployment of distilleries as ethanol price, algae productivity, and sugarcane cost. Furthermore, transportation costs play a decisive role in the geographic location of microalgae-based facilities and the distribution of their products.

Biofuels are considered an alternative to sustainable energy production due to their potential to reduce greenhouse gas emissions. This paper presents a mixed integer linear programming (MILP) model to evaluate the optimal configuration of a supply chain for the production of bioethanol, electricity and bagasse pellets from sugarcane potential in Colombia. The results show that gasoline demand in Colombia is met through bioethanol production, and the demand for coal used in thermoelectric plants can be met through the production of bagasse pellets from 17 biorefineries located in 13 study regions. The avoided emissions represent 25.17% of the target proposed by the Colombian government, and transport emissions represent only 2.62% of the emissions generated by the model. Despite the promising results obtained in the optimization of the supply chain for bioethanol and bagasse pellet production in Colombia, there are challenges and limitations that must be considered. One of the main challenges lies in the uncertainty associated with the variability in biomass, bioethanol, and carbon credit prices, which can affect the long-term economic viability of the project. The sustainability of land use for sugarcane production must be assessed with a more detailed approach to avoid conflicts with food production and ecosystem conservation. These aspects represent key opportunities for future research and improvements in strategic planning for the bioenergy sector. Finally, the sensitivity analysis shows that the ±20% variation in the price of sugarcane and the price of bioethanol have a high impact on the payback period with respect to the base case.