This project proposes a model for optimal and online tuning of PID controllers and evaluates its performance. The proposed model is based on computational intelligence approaches and can be used in industrial plant operating processes. The proposed tuning model is called PID neuro-fuzzy model, a formulation based on a structured artificial neural network and fuzzy rules. The neural network is used to perform an optimal adjustment of PID controller gains to ensure the operating point of the system, required to reduce the time of accommodation and the steady state error. A fuzzy system is incorporated into a real-time gain scheduling scheme to compensate for the possible variations in plant parameters. The performance of the proposed model is evaluated based on its ability to deal with uncertainties and disturbances in the process. The efficiency of the model is investigated through computational simulations in five plants of two industrial processes in the mining sector: i) solid bulk unloading processes by car dumper and ii) solid bulk resumption process by bucket wheel reclaimers. We conclude, the main advantage of the proposed model is adaptability related to variations in plant parameters during the operational process.