Abstract
This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7 for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4 using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%; and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%; and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.
Highlights
Neural networks were inspired by the power, flexibility and robustness of the biological brain
This is an indication that minimum errors were achieved when artificial neural networks based Monte Carlo (ANNBMC) was used for both the crude oil distillation column (CODC) design and controller prediction which corresponds to its training network architecture having lesser training error values with higher number of iteration steps in both task
The artificial neural networks based Monte Carlo simulation controller is effective for the predictions of the output variables and maximally relating the non-linear behaviour existing among various variables of the process
Summary
Neural networks were inspired by the power, flexibility and robustness of the biological brain. They are computational analogs of the basic biological components of a brain—neurons, synapses and dendrites. Artificial neural networks (ANN) consist of many simple computational elements (summing units—neurons—and weighted connections—weights) that work together in parallel and in series [1]. An ANN has the ability to learn relationships between given sets of input and output data by changing the weights. This process is called training the ANN [2]. While too many hidden neurons can hinder the ANN’s ability to generalize data not seen during training by causing over-fitting, too few hidden neurons can cripple its ability to learn the mapping at hand [6]
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