Several technological alternatives have been evaluated worldwide to mitigate the effects of the use and dependence on fossil raw materials. In this context, the so-called bioLPG has been considered a highly potential option with applications in the transportation, industrial, residential, and commercial sectors, particularly in the rural sector related to agribusiness. BioLPG presents, besides its renewable origin, other advantages such as relatively easier liquefaction under moderate pressure conditions (resulting in easier transportation, distribution, and storage) and higher energy density compared to natural gas or biomethane. However, for the production and commercialization processes of bioLPG to be effective from an economic and environmental perspective -analyzing costs and impacts -the entire production chain must be approached systematically, including the costs of raw material acquisition and transportation, as well as gas distribution and storage. Therefore, this study investigates the production of bioLPG through different technological routes: vegetable oil hydrogenation, glycerin dehydration, and biomass gasification. Detailed simulations of these routes were carried out using the Aspen Plus ® process simulator to evaluate their key indicators, such as product yield, emissions generation, raw material consumption, and energy consumption. The obtained results provided relevant datasets that relate the raw materials used, the technological routes employed, and the performance indicators of the processes. This information is essential for understanding and improving bioLPG production, considering both economic and environmental aspects. In addition to simulations, black-box models (Surrogate Models) were also used, which are representative mathematical models of bioLPG production processes, aiming to gain a better understanding of the sensitivity of the obtained indicators with respect to certain process variables. Thus, the objective of this work is to develop mathematical representation models of bioLPG production processes, either through steady-state computational simulations or Surrogate Models. These models can serve as archetypes for optimizing the bioLPG production chain, taking into account its economic and environmental dimensions, as well as its technical requirements.