Energy and stochastic economic assessment for distributed power generation from Manipueira biogas.

Cassava is a staple food in many countries, and this food source differs from other crops in that its processing generates a highly polluting and toxic residue (manipueira) that requires further treatment. The present study analyzed the economic feasibility of anaerobic digestion of manipueira for producing clean electricity through distributed generation (DG) while simultaneously eliminating toxic compounds. This eliminates the toxic residues. For this, an approach for the sizing of DG plants from manipueira biogas was presented, a non-trivial task which is not widespread in the literature. For two plants with different capacities, a deterministic economic analysis was carried out based on the criteria of Net Present Value, Internal Rate of Return, and Discounted Payback. Finally, the project risk was assessed through a sensitivity and stochastic analysis using Monte Carlo Simulation. The empirical verification was done on Brazilian data. When considering the NPV criterion, the results indicate a feasibility probability of 9.25% and 81.21% for scenarios 01 and 02, respectively. The results show that scale gains were important in reducing the impact of the investment and, at the same time, the larger scale of the project makes the cost of capital more relevant to the result. These findings show the need for subsidies for the investment, in addition to the promotion of specific credit lines that enable small-scale generation, or that can improve results in greater capacity.

Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion Processes.

The high proportion of CO2/CH4 in low aggregated value natural gas compositions can be used strategically and intelligently to produce more hydrocarbons through oxidative methane coupling (OCM). The main goal of this study was to optimize direct low-value natural gas conversion via CO2-OCM on metal oxide catalysts using robust multi-objective optimization based on an entropic measure to choose the most preferred Pareto optimal point as the problem's final solution. The responses of CH4 conversion, C2 selectivity, and C2 yield are modeled using the response surface methodology. In this methodology, decision variables, e.g., the CO2/CH4 ratio, reactor temperature, wt.% CaO and wt.% MnO in ceria catalyst, are all employed. The Pareto optimal solution was obtained via the following combination of process parameters: CO2/CH4 ratio = 2.50, reactor temperature = 1179.5 K, wt.% CaO in ceria catalyst = 17.2%, wt.% MnO in ceria catalyst = 6.0%. By using the optimal weighting strategy w1 = 0.2602, w2 = 0.3203, w3 = 0.4295, the simultaneous optimal values for the objective functions were: CH4 conversion = 8.806%, C2 selectivity = 51.468%, C2 yield = 3.275%. Finally, an entropic measure used as a decision-making criterion was found to be useful in mapping the regions of minimal variation among the Pareto optimal responses and the results obtained, and this demonstrates that the optimization weights exert influence on the forecast variation of the obtained response.