摘要
This review paper presents an in-depth investigation of the modeling techniques used to study conveyor belt dryers.These techniques are classified into four categories:theoretical modeling,computational fluid dynamics(CFD),empirical,and performance under different control strategies.Within the theoretical and CFD categories,the models are further classified as transient and steady state,as well as one-dimensional,two-dimensional,and three-dimensional.The empirical approach involves conducting experimental studies to collect moisture ratio data during the drying process and comparing it with empirical models.The methods of control are divided into classical and advanced controllers,with classical controllers including proportional-integral(PI),proportional-integral-derivative(PID),and quantitative feedback theory(QFT)controllers.Advanced controllers consist of artificial intelligence-based controllers,such as artificial neural networks(ANN),adaptive neuro-fuzzy inference systems(ANFIS),nonlinear autoregressive exogenous(NARX)models,model predictive control(MPC),and soft sensors.This review elucidated the methodologies and software employed for each modeling technique,as well as their prospective utility in industrial contexts.The utilization of theoretical and CFD methodologies is advantageous in forecasting the dynamics of complex systems.Conversely,empirical techniques serve the purpose of validating theoretical models and procuring data to facilitate model refinement.Controllers play a crucial role in the optimization of the drying process and the attainment of desired outputs.
基金
supported by the AmericanUniversity in Cairo,Egypt.
