Modeling pH neutralization processes using fuzzy-neural approaches
Fuzzy Sets and Systems
A hybrid fuzzy modeling method for short-term load forecasting
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
Modeling and control of a pilot pH plant using genetic algorithm
Engineering Applications of Artificial Intelligence
A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
Mathematical and Computer Modelling: An International Journal
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This paper proposes a hybrid model to identify the on-line pH characteristic of a neutralization plant. The hybrid model is the combination between neuro-fuzzy identification technique and first principle model. The neuro-fuzzy identification technique used training dataset to map the neutralization response curve in full ranges. The first principle model is based on material balances and chemical equilibrium equation. The objective of the proposed model is to extend the robustness effect for the on-line titration characteristic without having to re-design the model if the plant undergoes different conditions. In the experiment, the proposed model's dynamic response was compared with the on-line pH data. It showed the best fit for hybrid model with dynamic weight adjustment in nominal condition RSME = 0.1013 and in altered condition RMSE = 0.5616 proved it capability in capturing the additional variations to a pH neutralization plant.