Since the increase in clean energy demand is driven by environmental concerns, energy management is an ever-lasting issue globally. Among the different scenarios for energy manufacturing, the catalytic route through the famous process named Fischer-Tropsch Synthesis provides beneficial consequences including pollution reduction and economic efficiency, among others. In this regard, catalyst stability must be taken into account as a crucial performance parameter, especially in the expensive cobalt-catalyzed CO hydrogenation processes. As catalyst deactivation seems to be inevitable in catalytic processes, deactivation issues such as the extent, failure rate, or reactivation significantly influence the exploration, development, design, and operation of commercial processes. Accordingly, the deactivation trend of a cobalt-based catalyst was modeled via an innovative Weibull distribution base, which presents a significant advance over the existing macroscopic deactivation models. Being employed to obtain informative equations, the model parameters provide valuable information about the catalyst lifetime, which can be used as a useful predictive tool for industrial control purposes.
As the groundwater quantitative monitoring aimed to determine the factors affecting the aquifer behavior plays an important role in its regional management, studying the temporal and spatial groundwater level variations requires a comprehensive monitoring network. Effort has been made in this study to introduce a new linked simulation–optimization method, named MLPG-TLBO to quantitatively monitor the Birjand aquifer and determine the optimal points for piezometers. This model uses meshless local Petrov Galerkin (MLPF) method in the simulation part and teaching–learning-based optimization (TLBO) method in the optimization part. The objective function, in this study, is to minimize the difference between the groundwater level observed in piezometers and obtained computationally by the model. Since this coupled model is independent from the meshing, it eliminates the mesh-dependent shortcomings and, hence, yields more accurate results. It has been calibrated and validated in previous Birjand area studies and has led to acceptable results. By implementing the model in Birjand aquifer, the optimal positions of ten piezometers were determined mostly in areas where the density of the extraction wells was lower. Finally, the RMSE of both MLPG-TLBO and FDM was obtained to be 0.334 m and 1.483 m for 10 optimal piezometers. The RMSE value for MLPG-TLBO has shown a 0.423 m reduction compared to its previous value. This difference is quite meaningful as it shows good performance of this method in designing an optimal network for the aquifer.
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