The aim of the research was to develop and evaluate the usefulness of artificial neural network models for predicting the key operating parameters of centrifugal settlers. Various settler structures were analyzed, taking into account such elements as internal partitions and also inlet and outlet nozzles. Neural network modeling was continued until the highest possible quality was achieved in terms of training, testing and validation, with the occurrence of errors also being minimized. This process involved multiple iterations and adjustments of the network’s parameters to achieve optimal results. It was shown that artificial neural networks are characterized by having high accuracy in predicting the efficiency and damming values of centrifungal sedimentation tanks with regards to their design and hydraulic load. The designed network is able to determine both efficiency and liquid level with satisfactory accuracy.
The processing of waste cooking oils (WCO) is a complex process that heavily depends on their chemical and physical properties. The work described in this article includes research on the processing of WCO samples using transesterification methods. An automated reactor system Matrix9 HAAS was used to perform process studies and FT-IR measurements. Transesterification of oils in the presence of KOH might be directly applied as the optimal one for oils with a low acid number, while for oils with a higher acid number an additional esterification step is advisable in the presence of an acid catalyst or under conditions of high temperature and pressure.
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