Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling

AbstractDilute inorganic acids hydrolysis is one of the most promising pretreatment strategies with high recovery of fermentable sugars and low cost for sustainable production of biofuels and chemicals from lignocellulosic biomass. The diverse phenolics derived from lignin degradation during pretreatment are the main inhibitors for enzymatic hydrolysis and fermentation. However, the content features of derived phenolics and produced glucose under different conditions are still unclear due to the highly non-linear characteristic of biomass pretreatment. Here, an artificial neural network (ANN) model was developed for simultaneous prediction of the derived phenolic contents (CPhe) and glucose yield (CGlc) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (CIA), pretreatment temperature (T), residence time (t), solid-to-liquid ratio (RSL), kinds of inorganic acids (kIA), and enzyme loading dosage (E) were used as input variables. The CPhe and CGlc were set as the two output variables. An optimized topology structure of 6–12-2 in the ANN model was determined by comparing root means square errors, which has a better prediction efficiency for CPhe (R2 = 0.904) and CGlc (R2 = 0.906). Additionally, the relative importance of six input variables on CPhe and CGlc was firstly calculated by the Garson equation with net weight matrixes. The results indicated that CIA had strong effects (22%-23%) on CPhe or CGlc, then followed by E and T. In conclusion, the findings provide new insights into the sustainable development and inverse optimization of biorefinery process from ANN modeling perspectives. Graphical Abstract

Download Full-text

Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Artificial Neural Network Ann ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Download Full-text

A novel model for predicting fatty liver disease by means of an artificial neural network

Gastroenterology Report ◽

10.1093/gastro/goaa035 ◽

2020 ◽

Author(s):

Yi-Shu Chen ◽

Dan Chen ◽

Chao Shen ◽

Ming Chen ◽

Chao-Hui Jin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Liver Disease ◽

Diagnostic Accuracy ◽

Fatty Liver ◽

Fatty Liver Disease ◽

Health Check ◽

Ann Model ◽

Artificial Neural ◽

Input Variables

Abstract Background The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN. Methods A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen’s k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis. Results Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901–0.915]—significantly higher (P < 0.05) than that of the FLI model (0.881, 95% CI, 0.872–0.891) and that of the HSI model (0.885; 95% CI, 0.877–0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model. Conclusions Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.

Download Full-text

Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

Algerian Journal of Renewable Energy and Sustainable Development ◽

10.46657/ajresd.2021.3.1.5 ◽

2021 ◽

Vol 03 (01) ◽

pp. 45-52

Author(s):

Hadjira Maouz ◽

◽

Asma Adda ◽

Salah Hanini ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Thermal Aging ◽

Training Data ◽

Ethylene Propylene Diene Monomer ◽

Ann Model ◽

Ethylene Propylene ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

Download Full-text

Monitoring-System Development of Gillnet Using Artificial Neural Network

Volume 6B: Ocean Engineering ◽

10.1115/omae2020-19011 ◽

2020 ◽

Author(s):

Chungkuk Jin ◽

HanSung Kim ◽

JeongYong Park ◽

MooHyun Kim ◽

Kiseon Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Big Data ◽

Learning Algorithm ◽

System Development ◽

Ann Model ◽

Ann Models ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Download Full-text

Quantitative Relationship Analysis of Mechanical Properties with Mg Content and Heat Treatment Parameters in Al–7Si Alloys Using Artificial Neural Network

Materials ◽

10.3390/ma12050718 ◽

2019 ◽

Vol 12 (5) ◽

pp. 718 ◽

Cited By ~ 4

Author(s):

Xiaoyan Wu ◽

Huarui Zhang ◽

Haiyang Cui ◽

Zhen Ma ◽

Wei Song ◽

...

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Heat Treatment ◽

Artificial Neural Network ◽

Quantitative Relationship ◽

Ann Model ◽

Relationship Analysis ◽

Artificial Neural ◽

Input Variables ◽

Mg Content

In this paper, an artificial neural network (ANN) model with high accuracy and good generalization ability was developed to predict and optimize the mechanical properties of Al–7Si alloys. The quantitative correlation formulas of the mechanical properties with Mg content and heat treatment parameters were established based on the transfer function and weight values. The relative importance of the input variables, Mg content and heat treatment parameters, on the mechanical properties of Al–7Si alloys were identified through sensitivity analysis. The results indicated that the mechanical properties of Al–7Si alloys were sensitive to Mg content and aging temperature. Then the individual and the combined influences of these input variables on the properties of Al–7Si alloys were simulated and the process parameters were optimized using the artificial neural network model. Finally, the proposed model was validated to be a robust tool in predicting the mechanical properties of the Al–7Si alloy by conducting experiments.

Download Full-text

Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction

MATEC Web of Conferences ◽

10.1051/matecconf/202134303012 ◽

2021 ◽

Vol 343 ◽

pp. 03012

Author(s):

Iliass El Mrabti ◽

Abdelhamid Touache ◽

Abdelhadi El Hakimi ◽

Abderahim Chamat

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Goodness Of Fit ◽

Ann Model ◽

Goodness Of Fit Test ◽

Artificial Neural ◽

Input Variables ◽

Springback Prediction

In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.

Download Full-text

Prediction of osmotic coefficients for ionic liquids in various solvents with artificial neural network

Journal of the Serbian Chemical Society ◽

10.2298/jsc160725013d ◽

2017 ◽

Vol 82 (4) ◽

pp. 399-409

Author(s):

Cao Yu ◽

Shun Yao ◽

Xianlong Wang ◽

Tian Yao ◽

Hang Song

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ionic Liquids ◽

Mean Squared Error ◽

Osmotic Coefficients ◽

Back Propagation ◽

Ann Model ◽

Artificial Neural ◽

Input Variables ◽

Structural Descriptions

The relationship between the structural descriptions and osmotic coefficients of binary mixtures containing sixteen different ionic liquids and seven kinds of solvents has been investigated by back propagation artificial neural network (BP ANN). The influence of temperature on the osmotic coefficients was considered and the concentrations of ionic liquids were close to 1 mol kg-1, except in acetonitrile. Multi linear regression (MLR) was used to choose the variables for the artificial neural network (ANN) model. A three layer BP ANN with seven variables containing structural descriptions of the ionic liquids and the character of the solvent as input variables was developed. Compared with experimental data, the osmotic coefficients calculated using the ANN model had a high squared correlation coefficient (R2) and a low root mean squared error (RMSE).

Download Full-text

International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.723.854 ◽

2013 ◽

Vol 723 ◽

pp. 854-860 ◽

Cited By ~ 7

Author(s):

Ragaa Abd El-Hakim ◽

Sherif El-Badawy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Plain Concrete ◽

Ride Quality ◽

Ann Model ◽

Iri Model ◽

Roughness Index ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

nternational Roughness Index (IRI) is an important parameter that indicates the ride quality and pavement condition. In this study, an Artificial Neural Network (ANN) model was developed to predict the IRI for Jointed Plain Concrete Pavement (JPCP) sections. The inputs for this model are: initial IRI value, pavement age, transverse cracking, percent joints spalled, flexible and rigid patching areas, total joint faulting, freezing index, and percent subgrade passing No. 200 U.S. sieve. This data was obtained from the Long Term Pavement Performance (LTPP) Program. It is the same data and inputs used for the development of the Mechanistic-Empirical pavement Design Guide (MEPDG) IRI model for JPCP. The data includes a total of 184 IRI measurements. The results of this study shows that using the same input variables, the ANN model yielded a higher prediction accuracy (coeficint of determination: R2= 0.828, and ratio of standard error of estimate (predicted) to standard deviation of the measured IRI values: Se/Sy=0.414) compared to the MEPDG model (R2= 0.584, Se/Sy=0.643). In addition, the bias in the predicted IRI values using the ANN model was significantly lower compared to the MEPDG regression model.

Download Full-text

Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads

Energies ◽

10.3390/en14030756 ◽

2021 ◽

Vol 14 (3) ◽

pp. 756

Author(s):

Chanuk Lee ◽

Dong Eun Jung ◽

Donghoon Lee ◽

Kee Han Kim ◽

Sung Lok Do

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Efficient Operation ◽

Heating Systems ◽

Apartment Buildings ◽

Residential Heating ◽

Artificial Neural ◽

Input Variables ◽

Heating Loads

In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.

Download Full-text

Artificial Neural Network Prediction of Viruses in Shellfish

Applied and Environmental Microbiology ◽

10.1128/aem.71.9.5244-5253.2005 ◽

2005 ◽

Vol 71 (9) ◽

pp. 5244-5253 ◽

Cited By ~ 10

Author(s):

Gail Brion ◽

Chandramouli Viswanathan ◽

T. R. Neelakantan ◽

Srinivasa Lingireddy ◽

Rosina Girones ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Human Adenovirus ◽

Relative Strength ◽

Ann Model ◽

Human Virus ◽

Sensitivity And Selectivity ◽

Ann Models ◽

Artificial Neural ◽

Input Variables

ABSTRACT A database was probed with artificial neural network (ANN) and multivariate logistic regression (MLR) models to investigate the efficacy of predicting PCR-identified human adenovirus (ADV), Norwalk-like virus (NLV), and enterovirus (EV) presence or absence in shellfish harvested from diverse countries in Europe (Spain, Sweden, Greece, and the United Kingdom). The relative importance of numerical and heuristic input variables to the ANN model for each country and for the combined data was analyzed with a newly defined relative strength effect, which illuminated the importance of bacteriophages as potential viral indicators. The results of this analysis showed that ANN models predicted all types of viral presence and absence in shellfish with better precision than MLR models for a multicountry database. For overall presence/absence classification accuracy, ANN modeling had a performance rate of 95.9%, 98.9%, and 95.7% versus 60.5%, 75.0%, and 64.6% for the MLR for ADV, NLV, and EV, respectively. The selectivity (prediction of viral negatives) was greater than the sensitivity (prediction of viral positives) for both models and with all virus types, with the ANN model performing with greater sensitivity than the MLR. ANN models were able to illuminate site-specific relationships between microbial indicators chosen as model inputs and human virus presence. A validation study on ADV demonstrated that the MLR and ANN models differed in sensitivity and selectivity, with the ANN model correctly identifying ADV presence with greater precision.

Download Full-text