Optimization through artificial neural network on a programmable logic controller for a sludge drying plant

Abstract

The Sludge Drying Plant (SDP) is the final processing facility of Effluent Treatment System (ETS) that produces bio-sludge cake before it is sent out for final disposal. Due to the process disturbances, mechanical and inconsistent chemical reactions, the amount dry solid is reduced tremendously. The principal objective of this study is to derive a more realistic and reliable operational rules and algorithm through Artificial Modeling within the Programmable Logic Controller (PLC) for SDP, taking into account the controlled and uncontrolled parameters and system deciding the best operation per process. This study focuses on the identification, optimization, intelligent computing capability and technical operating skills that contribute to the SDP efficiency. Optimized controls for the SDP ensures a maximum weight percent solution (wt%) is squeezed out through complex computing from available process parameters.