Established Artificial Neural Network Model Research Articles

Post-earthquake repairability-based methodology for enhancing steel MRFs

Self-centering beam-to-column connections (SBCs) were widely investigated to enhance conventional steel moment-resisting frame’s (MRF’s) post-earthquake repairability by reducing residual displacements. The peak displacement-targeted design methodology was adopted to develop the design guidelines for enhancing MRFs with the emerging self-centering members, where the desired post-earthquake repairability of the enhanced MRF needs to be achieved with inevitable iterative dynamic analyses. This paper intends to develop a novel post-earthquake repairability-based methodology for enhancing MRFs with SBCs, where residual and peak displacements are both targets. To this end, artificial neural network (ANN) models were developed to estimate the peak and residual displacements of the enhanced MRFs with SBCs under given seismic intensities. Peak and residual displacement-based design (PRBD) procedures were developed based on the proposed post-earthquake repairability-based methodology and established ANN models. Four design cases were designed through the developed PRBD procedure and analyzed. The design and analysis outcomes demonstrate that the improved MRFs can attain the intended post-earthquake repairability and meet the peak and residual displacement objectives without necessitating iterative design and dynamic analyses. This confirms the effectiveness and precision of the proposed post-earthquake repairability-based approach and PRBD procedure. Moreover, the enhanced MRFs with partially self-centering behavior achieved nearly zero residual displacements (lower than 0.2%) under maximum considered earthquakes. This finding shows that the partially self-centering MRF is a more cost-efficient solution for developing seismic resilient MRF than fully self-centering MRF to achieve excellent post-earthquake repairability with a lower requirement of self-centering capacities.

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization.

This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Abstract Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and R 2 of 0.95, demonstrating adequate performance.

Experimental investigation and artificial neural network-based modelling of thermal barrier engine performance and exhaust emissions for methanol-gasoline blends

In recent years, due to environmental concerns and the depletion of fossil fuels, alternative fuel use and alternative emission reduction methods have gained importance in the automotive industry. In addition, methanol is used as an alternative fuel in gasoline engines with coated piston engines. This study first presents an experimental investigation of engine performance and exhaust emissions for a partially thermal barrier lined piston engine operating on methanol-gasoline blends. In the second phase the obtained data is then used to develop an Artificial Neural Network (ANN) based model to predict engine performance and exhaust emissions for methanol-gasoline blends. The developed ANN model was trained and validated using MATLAB. The results of the experimental study showed that the use of methanol-gasoline blended fuel in the engine provides better engine performance and reduced exhaust emissions compared to gasoline fuel. According to the results obtained, an increase of 3.7 % in effective power and a decrease in NOx and HC emissions by 19 % and 18 %, respectively, compared to the STD case when both coating and alternative fuel are used in the engine. With the established ANN models, engine performance parameters and exhaust emission parameters were predicted with 99 % and 98 % accuracy respectively.

Modelling and evaluation of the post-hardness and forming limit diagram in the single point incremental hole flanging (SPIHF) process using ANN, FEM and experimental

In the single point incremental hole flanging (SPIHF) process, a sheet material with pre-cut holes is deformed using the SPIF technique to generate a flange, making it an effective approach for low volume manufacturing and quick prototyping. In the case of the SPIHF technique, the post-forming hardness property, the forming limit diagram (FLD), and spring-back phenomena are not completely evaluated. To this end, this paper employs experimental investigation and numerical validation to analyse the impact of SPIHF process parameters like tool diameter, feed rate, spindle speed, and initial hole diameter on these aspects for the truncated incrementally formed components made from AA1060 aluminium alloy and DC01 carbon steel. The plasticity behaviour of both sheet metals was simulated using the Workbench LS-DYNA model and ANSYS software version 18. Additionally, Cowper Symonds power-law hardening was added to the model to account for material properties. The average post-hardness of AA1060 and DC01 was evaluated using an SPIHF prediction model based on the performance of an artificial neural network (ANN). This ANN model was developed using a feed-forward back-propagation network trained using the Levenberg-Marquardt approach. The ANNs 4-n-1 were created by varying the transfer functions and the number of hidden neurons. Greater spindle speed and bigger pre-cut holes were shown to significantly increase the post-formed hardness of the truncated components, whereas the converse was seen when using a higher feed rate and a larger tool diameter. In addition, the FLD and spring-back improved dramatically with larger hole diameters. Employing correlation coefficient (R) and mean square error (MSE) as validation measures, it was shown that the established ANN models accurately predicted the SPIHF process response. Both the DC01 and AA1060 neural network models with a 4-8-1 network architecture performed very well, with MSE and R values of 0.0000105 and 1 for DC01 and 0.02613 and 0.99982 for AA1061.

Intelligent upgrade of waste-activated sludge dewatering process based on artificial neural network model: Core influential factor identification and non-experimental prediction of sludge dewatering performance

Owing to the extremely complex compositions and origins of waste-activated sludge (WAS), the multiple physiochemical properties of WAS have impacts on its dewaterability, and there is a complex interaction relationship among the multiple physiochemical properties, which makes it difficult to identify the controlling factors on WAS dewaterability. Accordingly, there is still no unified certainty in the appropriate ranges of physiochemical properties for the optimal dewaterability of sludge from different sources, resulting in a lack of clear theoretical basis for technical selection and optimization of sludge dewatering processes. The large consumption of conditioning chemicals and low process efficiency stand for the major deficiency of existing sludge conditioning technologies. This study proposed to use a non-linear, adaptive and self-organizing artificial neural network (ANN) model to integrate the multiple physiochemical properties of WAS affecting its dewaterability, and WAS dewatering performance under certain conditioning schemes could be predicated by ANN model with the multiple physiochemical properties and conditioning operation parameters as the input arguments. Thus, the laborious filtration experiments for screening conditioning chemicals could be replaced by the input adjustment of ANN model. Rooted mean squared error (RMSE) of 6.51 and coefficient of determination (R2) of 0.73 confirmed the satisfied stability and accuracy of established ANN model. Furthermore, the predictor-exclusive method revealed that the exclusion of polar interface free energy decreased most, which reflected the importance of surface hydrophilicity reduction in sludge dewaterability improvement. All the contributions presented here were believed to provide an intelligent insight to improve the experience operation status of WAS dewatering process.

Artificial Neural Network Analysis of Determinants of Tacrolimus Pharmacokinetics in Liver Transplant Recipients.

The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations. This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP). In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C0) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C0 of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C0. A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C0 values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C0: age, daily drug dose, genotype at CYP3A5 polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C0 value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C0 was higher when recipients and donors had the genotype CYP3A5*3*3 than when they had the genotype CYP3A5*1. The predicted C0 value also increased with the use of caspofungin or Wuzhi capsule. The established ANN model can be used to predict the C0 value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.

Controlling cathepsin B activity in Jinhua ham through multifactorial analysis and modeling of temperature, pH and salt content

The activity of cathepsin B in Jinhua ham was affected by various processing parameters, which were investigated and quantified using a multi-layer artificial neural network (ANN). The key factors that influence ham quality are processing temperature, pH, and sodium chloride content. The study discussed the univariate analysis and multivariate interactions between two or three factors. The established ANN model was used for further investigation, and the actual enzyme activity of cathepsin B was characterized. The results showed that high temperature (greater than 60 °C) and an alkaline environment or the addition of Na + cation significantly reduced the enzyme activity (p < 0.05). The optimal conditions for protease activity were found to be at 40 °C and pH 6.0. The enzyme was found to be unstable at a temperature of 20 °C or pH 6.5–7.0 in the presence of NaCl, indicating significant interactions between temperature, pH value and NaCl content (p < 0.01). Furthermore, the ANN model showed good match between true and predicted values. The correlation coefficient was 0.98468, and the model provided supplementary information about the effect of NaCl on cathepsin B activity within 0.4M–0.6M. The findings could be useful in Jinhua ham production and the study of the structure-activity relationship of cathepsin B.

Modification of Values for the Horizontal Force of Tillage Implements Estimated from the ASABE Form Using an Artificial Neural Network

The famous empirical model for the horizontal force estimation of farm implements was issued by the American Society of Agricultural Biological Engineers (ASABE). It relies on information on soil texture through its soil texture adjustment parameter, which is called the Fi -parameter. The Fi-parameter is not measurable, and the geometry of the plow through the machine parameter values are not measurable; however, the tillage speed, implement width, and tillage depth are measurable. In this study, the Fi-parameter was calibrated using a regression technique based on a soil texture norm that combines the sand, silt, and clay contents of a soil with R2 of 0.703. A feed-forward artificial neural network (ANN) with a backpropagation algorithm for training purposes was established to estimate the modified values of the horizontal force based on four inputs: working field criterion, soil texture norm, initial soil moisture content, and the horizontal force (which was estimated by the ASABE standard using the new—Fi-parameter). Our developed ANN model had high values for the coefficient of determination (R2) and their values in the training, testing, and validation stages were 0.8286, 0.8175, and 0.8515, respectively that demonstrated the applicability for the prediction of the modified horizontal forces. An Excel spreadsheet was created using the weights of the established ANN model to estimate the values of the horizontal force of specific tillage implements, such as a disk, chisel, or moldboard plows. The Excel spreadsheet was tested using data for a moldboard plow; in addition, a good prediction of the required horizontal force with a percentage error of 10% was achieved. The developed Excel spreadsheet contributed toward a numerical method that can be used by agricultural engineers in the future. Furthermore, we also concluded that the equations presented in this study can be formulated by any of computer language to create a simulation program to predict the horizontal force requirements of a tillage implement.

Insight into the synergistic performance during ex-situ catalytic co-pyrolysis of poplar sawdust and polypropylene over Fe-Ni/ZSM-5 for the enhancement of aromatics

Hydrogen-deficient and oxygen-rich biomass result in low hydrocarbon production during pyrolysis. This work aims to enhance aromatics in bio-oil through ex-situ catalytic co-pyrolysis of poplar sawdust (PS) and polypropylene (PP) over Fe-Ni/ZSM-5 and gain an understanding of the synergistic pyrolysis performance. The results showed that adding PP improved the comprehensive pyrolysis performance of the PS and PP blends. The deviation between the experimental and calculated mass loss of the blends confirmed the interaction between PS and PP, with a more positive synergistic effect at a biomass/plastic mass ratio of 3:2. The average activation energy of PP was 211.6 kJ·mol−1, which is 28.2 % higher than that of PS. As the proportion of PP increases, the activation energy required for the blends increases. The established artificial neural network model accurately predicts the mass loss of the blends (R2 ≥ 0.999) by providing the mixing ratio, heating rate, and temperature. The addition of hydrogen-rich feedstock increased aromatic yield in the volatile products, indicating the positive synergy between PS and PP during catalytic co-pyrolysis over Fe-Ni/ZSM-5. As the targeted products among the aromatics, the total yields of benzene, toluene, and xylene ranged from 58.43 to 251.03 mg·g−1 in the PS and PP blends, respectively.

Artificial neural network for mechanism identification and process prediction of the nanoclusters deagglomeration in the in-line HSM

A multiple-input multiple-output artificial neural network (ANN) for the mechanism identification of nanoclusters deagglomeration and the prediction of particle size distribution (PSD) evolution of nanocluster in deagglomeration process of in-line high shear mixer (HSM) was successfully established. It can successfully realize the description of nanoclusters deagglomeration process within single or multiple processing batches. Besides, this ANN model also can realize the accurate prediction of the d32 of fine and coarse agglomerates, and the fines fraction, at different operating conditions and structural parameters, within the relative error of 10 % and 20 % respectively. Meanwhile, 3D profiles of PSD versus different operating conditions and structural parameters were output by the established ANN model, which were useful for parameter selection of in-line HSM to realize efficient production of nanoparticles suspension. The successful establishment of this ANN model provides an effective, flexible, and advanced intelligent method for guiding the parameter selection of in-line HSM, for realizing the controllable and efficient production of the high-end nanoparticle products with specific particle characteristics.

Identification of the reaction network for the synthesis of adipic acid using machine learning coupling with target factor analysis

AbstractThis study investigated the reaction network of the oxidation of cyclohexanone with nitric acid through machine learning (ML) model coupling with target factor analysis (TFA). Experiments for the synthesis of adipic acid (AA) were carefully designed and carried out in a microreactor system. An artificial neural network (ANN) model was applied and optimized by training on experimental data. To assess the established ANN model, a comparison between the predicted concentrations of the products and those calculated with the power law kinetic model was implemented. TFA was then performed on both the experimental data and the data simulated by ANN for identifying the candidate reactions and finding out the temperature boundaries. Based on the identification results of the reaction network, the kinetic characteristics of this oxidation process under various operational conditions were further researched through the incremental approach and simultaneous approach.

Artificial neural network model of catalytic coal gasification in fixed bed

Catalytic coal gasification is a cost-effective way to utilize coal to produce hydrogen or methane. Because of complex interactions among hydrodynamics and chemical reactions with catalyst, it was very difficult to predict hydrogen yield and carbon conversion. Therefore, three kinds of artificial neural network (ANN) models, including feed-forward back propagation neural network (FFBP), cascade-forward back propagation neural network with Levenberge Marquardt algorithms (CFBP), and cascade-forward back propagation neural network with genetic algorithm (CFBP-GA) were used to predict these processes. Three kinds of input parameters were used, such as coal ultimate analyses, coal proximate analyses and operation conditions. Gas yield and carbon conversion were taken as output parameters. R2 of all three established ANN models were above 0.9. The CFBP-GA showed good performance with E2 = 0.000241 and R2 = 0.9978 than the other two ANN models. And then, the model was used to predict the effects of temperature, catalyst type and catalyst loading on carbon conversion and hydrogen yield. The most important three factors for carbon conversion and hydrogen yield were catalyst type, catalyst loading and temperature based on relative importance analysis. The good performances were indicated the ANN model was an effective way to predict hydrogen production and carbon conversion of catalytic coal gasification in fixed bed quickly and accurately, and it could be also extended to other carbon resources to produce hydrogen.

Evaluation of thin film material properties using a deep nanoindentation and ANN

Due to the substrate effect, there are several difficulties to evaluate the material properties of thin films via nanoindentation. In this study, an inverse analysis method based on an artificial neural network (ANN) is proposed to obtain free-volume-model (FVM) parameters of thin film metallic glass (TFMG) via nanoindentation. Unlike conventional nanoindentation procedures for thin films, a deeper indentation depth (≈ 30% of film thickness) is adopted to accurately identify the film properties even with significant substrate deformations. Both sphero-conical and Berkovich tips are employed to ensure unique solutions. A complex mapping function of inverse analysis is replaced by establishing ANN between nanoindentation (features) and material (targets) parameters. The established ANN model is trained with the database generated via systematic finite element analyses (FEA). The trained ANN model is experimentally validated by estimating the material properties of Zr55Cu30Ag15 TFMG deposited on two different substrates (Si and soda lime glass). The maximum difference of plastic indentation energy between experiments and FEA values using the estimated film material properties was within 3%.

High-temperature deformation characteristics and constitutive models of Inconel 625 superalloy

The isothermal compressive tests are performed to analyze the hot deformation characteristics of Inconel 625 superalloy over a wide temperature range of 1000–1150 °C and the strain rate scope of 0.01–10 s−1. The hot deformation mechanisms and the impacts of deformation parameters on the thermal deformation features, are analyzed. It reveals that the true stress first rises to a maximum stress level and then progressively drops to a comparatively stable level, demonstrating apparent work-hardening (WH) and dynamic softening (DS) features. Meanwhile, the true stress swiftly rises with the reduced deformation temperature or the increased strain rate. Moreover, the almost fine and uniform grains surround the whole microstructure under relatively large true strain, elevated temperature, and depressed strain rate. Besides, three kinds of constitutive models, including the Arrhenius-type (AT) model, physical-based (PB) model, and artificial-neural-network (ANN) model, complied with the backpropagation learning (BP) and new binary particle swarm optimization (NBPSO) algorithms are developed, respectively. The correlation coefficient (R) and absolute relative error (ARE) distribution are estimated to assess the predictive abilities of the established models. It can be concluded that the maximum absolute relative errors are near 12%, 15%, and 7.2% for AT, PB, and NBPSO-BP ANN models, respectively. Meanwhile, the correlation coefficient of the NBPSO-BP ANN model is as high as 0.9983, while the corresponding ones for the AT and PB models are 0.9951 and 0.9903, respectively. Thus, the established NBPSO-BP ANN model can depict the hot deformation features of the studied superalloy precisely.

Artificial Neural Network Model for the Prediction of Methane Bi-Reforming Products Using CO2 and Steam

The bi-reforming of methane (BRM) is a promising process which converts greenhouse gases to syngas with a flexible H2/CO ratio. As there are many factors that affect this process, the coupled effects of multi-parameters on the BRM product are investigated based on Gibbs free energy minimization. Establishing a reliable model is the foundation of process optimization. When three input parameters are changed simultaneously, the resulting BRM products are used as the dataset to train three artificial neural network (ANN) models, which aim to establish the BRM prediction model. Finally, the trained ANN models are used to predict the BRM products when the conditions vary in and beyond the training range to test their performances. Results show that increasing temperature is beneficial to the conversion of CH4. When the molar flow of H2O is at a low level, the increase in CO2 can enhance the H2 generation. While it is more than 0.200 kmol/h, increasing the CO2 flowrate leads to the increase and then decrease in the H2 molar flow in the reforming products. When the numbers of hidden layer neurons in ANN models are set as (3, 3), (3, 6) and (6, 6), all the correlation coefficients of training, validation and test are higher than 0.995. When these ANN models are used to predict the BRM products, the variation range of the prediction error becomes narrower, and the standard deviation decreases with the increase in neuron number. This demonstrates that the ANN model with more neurons has a higher accuracy. The ANN model with neuron numbers of (6, 6) can be used to predict the BRM products even when the operating conditions are beyond the training ranges, demonstrating that this model has good extension performance. This work lays the foundation for an artificial intelligent model for the BRM process, and established ANN models can be further used to optimize the operating parameters in future work.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Investigation on the co-pyrolysis of agricultural waste and high-density polyethylene using TG-FTIR and artificial neural network modelling

To realize utilization of agricultural and plastic waste to alleviate environmental pollution, the individual pyrolysis and co-pyrolysis characteristics of kidney beans stalk (KS) and high-density polyethylene (HDPE) were investigated. Thermogravimetry coupled with Fourier transform infrared spectroscopy (TG-FTIR) was used to investigate the pyrolysis behaviour, synergistic effect, kinetics and gaseous product evolution of different samples. In addition, an artificial neural network (ANN) model was established to predict the mass change with temperature during sample pyrolysis or co-pyrolysis. The results showed that the decomposition of HDPE was easier than that of KS, and synergistic and inhibitive effects occurred during co-pyrolysis. The synergistic or inhibitive effect was most significant from 470 to 510 ℃. The FTIR analysis results showed that gaseous products of KS pyrolysis were mainly oxygen-containing compounds including CO2, CO, ketones, aldehydes, esters, etc., while those of HDPE pyrolysis were mainly hydrocarbons including alkanes, alkenes, aromatic rings, etc. The co-pyrolysis of samples with different proportions promoted or inhibited the production of some gaseous products to different degrees. Moreover, the activation energy of the two stages during co-pyrolysis was lower than that of the pure sample. The established ANN model can effectively predict the mass loss of a sample with temperature.

Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface.

Landfills release significant odorous compounds from the working surface, and their emission rates are crucial for odor and health risk assessment. A total of 99 valid datasets of odor emissions from a landfill working surface were obtained from in situ monitoring for 9months. Meteorological parameters (temperature, humidity, atmospheric pressure) and waste properties (contents of protein, lipid, carbohydrate, ash, and moisture) were used to construct artificial neural network (ANN) models for the emission rate prediction of typical compounds. The optimal structures and performance of the ANN models were determined by comparing and training with different structural configurations. The ANN models with genetic algorithm (GA) optimization show better performance than those without GA. With the data distribution of input parameters, the ranges of the emission rates of typical compounds were predicted by combining the established ANN models and the Monte Carlo approach. The sensitivity and uncertainty analyses revealed that temperature, atmospheric pressure, protein and lipid contents are parameters sensitive to emission rates, and meteorological parameters have significant impacts on the uncertainty. The established ANN models for the prediction of emission rates can provide scientific evidence and an approach to assess and control the odor and health risk in waste sectors.

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.