Back propagation neural network model for predicting the performance of immobilized cell biofilters handling gas-phase hydrogen sulphide and ammonia

An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process

The purpose of this work was to develop a problem-solving approach and a simulation tool that is useful for the specification of wastewater treatment process equipment design parameters. The proposition of using an artificial neural network (ANN) numerical model for supervised learning of a dataset and then for process simulation on a new dataset was investigated. The effectiveness of the approach was assessed by evaluating the capacity of the model to distinguish differences in the equipment design parameters. To demonstrate the approach, a mock dataset was derived from experimentally acquired data and physical effects reported in the literature. The mock dataset comprised the influent flow rate, the bed packing material dimension, the type of packing material and the packed bed height-to-diameter ratio as predictors of the calorific value reduction. The multilayer perceptron (MLP) ANN was compared to a polynomial model. The validation test results show that the MLP model has four hidden layers, each having 256 units (nodes), accurately predicts calorific value reduction. When the model was fed previously unseen test data, the root-mean-square error (RMSE) of the predicted responses was 0.101 and the coefficient of determination (R2) was 0.66. The results of simulation of all 125 possible combinations of the 3 mechanical parameters and identical influent wastewater flow profiles were ranked according to total calorific value reduction. A t-test of the difference between the mean calorific value reduction of the two highest ranked experiments showed that the means are significantly different (p-value = 0.011). Thus, the model has the capacity to distinguish differences in the equipment design parameters. Consequently, the values of the three mechanical feature parameters from the highest ranked simulated experiment are recommended for use in the design of the industrial scale upflow anaerobic filter (UAF) for wastewater treatment.

Application of artificial neural networks for predicting the isotopic composition of high burn-up solid plutonium sample using the 90–105 keV gamma-spectrum region

An artificial neural network (ANN) algorithm was developed to predict isotopic composition of five Pu isotopes (238Pu, 239Pu, 240Pu, 241Pu, and 242Pu) of high burn-up Pu samples. The study was carried out using the most complex but informative gamma energy region of Pu gamma spectra, 90–106 keV. This region has remained futile, due to the overlapping nature of the gamma emission lines and X-rays emitted by U, Pu, and Np. A backpropagation neural network algorithm based ANN with error minimization using the steepest gradient method was built with the help of normalized gamma spectra for ∼800 samples. The paper discusses the optimization of hidden neuron number and the layer design for best prediction. With the exception of 242Pu, the prediction accuracy and precision of the proposed technique was found to be ∼3% for all other isotopes of Pu.

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Introduction

A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure.

Methods

We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria.

Results

Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer.

Conclusion

The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery wastewater in a hybrid upflow anaerobic sludge blanket reactor

In a hybrid upflow anaerobic sludge blanket (HUASB) reactor, biodegradation in association with biohydrogen production was studied using distillery wastewater as substrate. The experiments were carried out at ambient temperature (34±1°C) and acidophilic pH of 6.5 with constant hydraulic retention time (HRT) of 24h at various organic loading rates (OLRs) (1-10.2kgCODm(-3)d(-1)) in continuous mode. A maximum hydrogen production rate of 1300mLd(-1) was achieved. A back propagation neural network (BPNN) model with network topology of 4-20-1 using Levenberg-Marquardt (LM) algorithm was developed and validated. A total of 231 data points were studied to examine the performance of the HUASB reactor in acclimatisation and operation phase. The statistical qualities of BPNN models were significant due to the high correlation coefficient, R(2), and lower mean absolute error (MAE) between experimental and simulated data. From the results, it was concluded that BPNN modelling could be applied in HUASB reactor for predicting the biodegradation and biohydrogen production using distillery wastewater.