Artificial Neural Network Tool Research Articles

An artificial neural network tool to support the decision making of designers for environmentally conscious product development

The consideration of sustainability aspects in initial stages of product development is understood as an effective approach to plan a sustainable product life cycle. It can be achieved by integrating the sustainability considerations into decision making of companies in early design phases. This study aims to develop an Artificial Intelligence (AI) tool that can assist the designers in their decision making to choose environmentally benign design parameters of products. The proposed tool is based on an Artificial Neural Network (ANN) model which takes the life cycle design parameters (viz. size of product, density of material, manufacturing process, transport mode and recyclability) as inputs and provides the corresponding outputs in terms of ‘carbon footprint’ and ‘life cycle cost’ of a product. These outputs assist the designers to realize the trade-off between the environmental load and cost effectiveness of a design alternative. Thus, it enables the decision making of companies to select a more sustainable design. A Graphical User Interface (GUI) is developed for the AI tool so that the designers can efficiently use this tool. The results predicted by the proposed tool are compared with the results obtained through the life cycle assessment carried out by using GaBi 9.2. The comparison shows that the results predicted by the tool have a reasonable accuracy of more than 90% which is significant, especially in the design stages of environmentally conscious product development. Also, the time efficiency of the tool to compute the environmental impact was compared with that of GaBi 9.2 by using a T-test. Results showed that the time efficiency of the proposed AI tool is significantly higher than that of GaBi 9.2.

Artificial neural networks in valorization process modeling of lignocellulosic biomass

AbstractValue‐added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value‐added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre‐treatment processes (microwave‐assisted, organosolv‐, ultrasound‐assisted pre‐treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime

The fretting problem occurs at two contact surfaces sustaining small relative displacement, and it reduces the fatigue lifetime dramatically. Estimating accurate fretting fatigue lifetime plays an important role in engineering applications. Due to the complicated stress state, and high-stress gradient in the contact surface, the average methods are necessary to obtain the precise lifetime, but the critical distance for the average zone is difficult to estimate. In this work, Artificial Neural Network (ANN) tool combined with damage parameters is proposed to determine the optimal critical distance for different fretting conditions. This tool can also be used to accurately predict the crack initiation lifetime. The fretting fatigue lifetimes calculated by using this approach have shown good agreement with experimental results from literatures. In addition, rough estimates of critical distance for different cases are made based on the numerical results.

Neural network based uncertainty and sensitivity evaluation of electrical resistivity tomography for improved subsurface imaging

Assessment of subsurface status by resistivity technique, being an indirect approach, is pretended to be a strategic factor. Projection of full proof confirmation in this domain is always a challenge and hence outcomes are expressed in possibilities. Intervene of mathematical interpretation on resistivity data generated in the field by different arrays would offer a better choice in building-up the possibility of projecting the actual status. Thus, a study of Wenner-Schlumberger (WS), dipole–dipole (DD) and combined inversion (CI) data of three parallel profiles have been conducted, as a whole, for old and abandoned shallow depth coal mine workings in Jharia coalfield. The study recapitulates influence of sensitivity and uncertainty with depth, apart from resistivity. Statistical significance of the data has been evaluated inclusive of their inter-relationship. PCA presented an encouraging relation of sensitivity with depth. The comprehensible approach of mathematical interpretation helps in cracking a problem of uncertain prediction. Sensitivity and the extent of uncertainty are the parameters to build a strong foundation for evaluating the degree of confidence in prediction accuracy. Artificial Neural Network (ANN) tool has been used to understand the relative importance of sensitivity and uncertainty with depth. The weightage of sensitivity has been observed to be on upper side with respect to uncertainty. The importance of configuration of resistivity survey array has been emphasized based on sensitivity.

Prediction of Groundwater Quality Index in the Selected Divisions of Srikakulam Using Artificial Neural Networks Approach

Applicability of artificial neural network (ANN) modelling in predicting the water quality index (WQI) and in turn to ascertain the suitability of the water for human consumption has been presented in the paper. In the light of the present study, seventy-nine (79) groundwater samples were collected from two mandals (divisions) Veeraghattam (VGT) and Palakonda (PLKD) and analyzed for physicochemical parameters during the pre-monsoon and post-monsoon seasons of 2015 and 2016. In computing the WQI, physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, chlorine, fluoride, nitrite, DO and TA have been considered. From the results it was found that the WQI varies from 43.9 to 46.5 and 31.4 to 34.7 in VGT and PLKD divisions respectively. ANN tool in MATLAB has been used to predict the WQI. Back propagation methodology and LM algorithm has been chosen for the study. To train the network, physicochemical parameters have been given as inputs and the already computed WQI values as output. A particular season has been chosen for testing the network. After simulating the network, the results obtained were compared with the experimental value and found to have an error of 0.6%. It is inferred that the chosen model fits apt for the prediction of WQI in the present study.

Damage assessment of aircraft wing subjected to blast wave with finite element method and artificial neural network tool

Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network (BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method (FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.

Gamma-ray energy spectrum unfolding of plastic scintillators using artificial neural network

In this study, the unfolding of the plastic scintillator spectrum was undertaken using the artificial neural networks tools of MATLAB. To this purpose, the response matrix of the plastic scintillator was generated for 145 energy groups and in 512 pulse-height channels using the MCNPX2.6 code. The results confirmed that the relative error in the gamma-ray energy unfolding with artificial neural networks is less than 3.8%.

FC048: New Tool to Predict the Clinical Course and Renal Failure in Patients with Immunoglobulin a Nephropathy

Abstract BACKGROUND AND AIMS Idiopathic Immunoglobulin A nephropathy (IgAN) is the most common biopsy-proven glomerulonephritis in the world. Approximately 40% of IgAN patients reach renal failure (RF) 20 years after their kidney biopsy. The high prevalence of RF shows that IgAN has a significant economic impact in the countries because renal replacement therapy is costly. Moreover, the disease's onset in the second and third decades of life represents a social challenge because patients are typically very active and highly productive in the workplace. This challenge is one more reason to move on the prediction of the clinical course and RF in IgAN patients at the time of the kidney biopsy and during the follow-up. We developed an artificial neural network (ANN) tool (DialCheck 1.0) based on seven variables and the histological score of the kidney biopsy to predict RF in IgAN patients at the time of kidney biopsy [1]. METHOD We have recently developed a new tool which consists of a set of ANN-powered models that combine temporally accurate observations for fine-grained features and leverage state-of-the-art deep neural network techniques to forecast the patient's clinical evolution. RESULTS A cohort of 948 IgAN patients, of whom the clinical course of the disease was known, was used to develop the new tool that predicts the dynamics of age and disease laboratory parameters (serum creatinine, daily proteinuria), blood pressure, histological score of the kidney biopsy and the RF. The system was designed to help the physicians give a broader spectrum of information regarding the patient and the potential clinical development of IgAN and outcomes. In detail, the model computes a latent dynamic representation of the patient and predicts a prospective clinical picture of the patient and the probability of RF. The tool with an accuracy of >80% will be tested in an independent retrospective cohort of 454 IgAN patients and in a prospective multicenter randomized clinical study in which 426 IgAN patients will be enrolled in two different groups based on the type of kidney lesions (active and chronic renal lesions). CONCLUSION We have a new tool (DialCheck 2.0), based on ANN, that may predict the outcome and the RF in IgAN patients. Moreover, it may help the physician to analyze the long-term response to therapy.

RSM, ANN and ANFIS applications in modeling fermentable sugar production from enzymatic hydrolysis of Colocynthis Vulgaris Shrad seeds shell

The artificial neural network (ANN), response surface methodology (RSM), and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict the yield of fermentable sugar from Colocynthis vulgaris Shrad seeds shell (CVSSS). Enzymatic hydrolysis was done by applying Aspergillus Niger. The ANN, RSM, and ANFIS models got investigated based on; hydrolysing temperature, time and pH as input variables, whereas the percentage yield of sugar was the response factor. Statistical error tasks were additionally, applied to relate the adequacy of the models. The result showed that, sugar can be obtained from CVSSS. The ANFIS, ANN, and RSM tools presented a nigh perfection, in predicting the yield of fermentable sugar from CVSSS with R squared value of 0.9986, 0.9978, and 0.9975, correspondingly. Additional statistical guides gave acceptance to ANFIS and RSM as the best and the least predictive tools respectively. Optimization result with ANFIS tool, presented an optimum hydrolysis productivity of 60.65%.

Data-driven analysis on axial strength of GFRP-NSC columns based on practical artificial neural network tool

Previous research is deficient in the theoretical estimation of the axial load-carrying strength (ALCS) of glass fiber reinforced polymer (GFRP) composite columns. This study aims to calculate the ALCS of GFRP normal strength concrete (NSC) columns (GFRP-NSC) using two different approaches; empirical modeling and artificial neural networks (ANNs). The geometric, material, and testing details of 250 GFRP-NSC columns were collected from the previous studies. An initial assessment of the previous strength models for the ALCS of GFRP-NSC columns was accomplished over the constructed database. General regression and curve-fitting were used for suggesting an improved empirical model. The various hidden layers and neurons were calibrated to find an improved ANN model. Both new empirical and ANN models showed higher accuracy than the previous model for capturing the ALCS with R2 = 0.740, and R2 = 0.882, individually. A comparison of the predictions through one-way analysis of variance (ANOVA) at a 5% significance level depicts that the proposed models presented a higher accuracy than the previous models for capturing the ALCS of GFRP-NSC columns. Additionally, a detailed parametric investigation of GFRP-NSC members was carried out using the suggested empirical model to explore the influence of different variables of studied members.

Development of multiple linear regression, artificial neural networks and fuzzy logic models to predict the efficiency factor and durability indicator of nano natural pozzolana as cement additive

The current study aims at predicting the efficiency factor (EF) and durability indicator (DI) of the natural pozzolana when added as a cement replacement at nano scale. Multiple linear regression (MLR), artificial neural networks (ANN) and fuzzy logic (FL) tools were employed in the analytical study. The studied sample was the data collected from an experimental study carried out by the authors, on a local natural pozzolana. Curing time, nano natural pozzoalna (NNP) content, median particle size of natural pozzolana, water/binder (w/b) ratio and the superplasticizer dosage were selected as input variables. The curing times were investigated at 2, 7, 28, 90 and 180 days. NNP was added at six percentages, i.e. 0, 1, 2, 3, 4 and 5%. Two median particle sizes i.e. 100 and 500 nm, were studied. Four w/b ratios and four superplasticizer dosages were examined, i.e. 0.4, 0.5, 0.6 and 0.7, and 0, 0.5, 2 and 4 l/m3, respectively. The correlation coefficients and several performance criteria were computed to evaluate the developed models. Based on the analysis results, it can be concluded that EF and DI of NNP can be effectively predicted using ANN and FL techniques. The results obtained by MLR were far from those obtained by both ANN and FL. In addition, ANN tool was slightly more accurate than FL as far as prediction of EF is concerned. Coefficient of determination (R2) values of 0.992, 0.987 and 0.651 along with mean absolute percentage error (MAPE) values of 18.5, 2.3 and 3.7 were the outcome of ANN, FL and MLR models, respectively, when EF was predicted. Further, the re-evaluation of this study can be done in future, particularly when more data is available in the literature.

Revealing prediction of perched cum off-centered wick solar still performance using network based on optimizer algorithm

A sincere effort has been made to engineer a perched cum off-centered wick solar still (PCWSS) in the present work. The daily average efficiency with hourly prediction distillate yield of the PCWSS has used those artificial neural networks (ANNs) tool with Harris Hawk’s Optimizes (HHOs) technique. HHO performance with ANN simulated as an optimal parameter to grab preyed. An experimental performance predicting the system's productivity is associated by dual supplementary mockups as vectors gadget, tradition ANN. HHO-ANN approach results are compared with the experimental observations (one year) of the solar still. Radial Basis Function (RBF) and Feed Forward (FF) have been used ANN structures to estimate hourly distillate yield and efficiency of the system is 59.78%. Evaluating the R2, RMSE, MRE, MAE, EC, OI, CRM analysis of prediction models was based on numerical error conditions. Optimized the analysis of PCWSS with a model as HHO-ANN used optimal parameter values has prediction accuracy associated with ANN and the competence for HHO. Annual analysis based on the HHO - ANN structures predicted the hourly distillate yield with mean error varying from 8.13% and 6.1%. The error for the monthly average prediction of distillate yield is from 0.95% to 1.12%, respectively. HHO – ANN has been used with the best accuracy in predicting the PCWSS invention associated with tangible experimental outcomes.

Discrimination between inrush and fault currents of transformers using Artificial Neural Network Tools

Discrimination between inrush and fault currents of transformers using Artificial Neural Network Tools

Predicting Slump Values of Concrete Made by Pozzolans and Manufactured Sand using ANN

Large amounts of natural fine aggregate (NFA) and cement are used in building, which has major environmental consequences. This view of industrial waste can be used in part as an alternative to cement and part of the sand produced by the crusher as fine aggregate, similar to slag sand (GGBFS), fly ash, metacaolin, and silica fume. Many times, there are issues with the fresh characteristics of concrete when using alternative materials. The ANN tool is used in this paper to develop a Matlab software model that collapses concrete made with pozzolanic material and partially replaces natural fine aggregate (NFA) with manufactured sand (MS). Predict. The slump test was carried out in reference with I.S11991959, and the findings were used to create the artificial neural network (ANN) model. To mimic the formation, a total of 131 outcome values are employed, with 20% being used for model testing and 80% being used for model training. 25 enter the material properties to determine the concrete slump achieved by partially substituting pozzolan for cement and artificial sand (MS) for natural fine aggregate (NFA). According to studies, the workability of concrete is critically harmed as the amount of artificial sand replacing natural sand grows. The ANN model's results are extremely accurate, and they can forecast the slump of concrete prepared by partly substituting natural fine aggregate (NFA) and artificial sand (MS) with pozzolan.

Microwave vacuum drying of pomegranate peel: Evaluation of specific energy consumption and quality attributes by response surface methodology and artificial neural network

Microwave vacuum drying of pomegranate peel was studied through this work and the drying process was modeled with the aid of response surface methodology (RSM) and artificial neural network (ANN) method. The drying experimental runs were performed on varied ranges of microwave power (175, 330, and 485 W) and vacuum pressure (10, 15, and 20 kPa) using a face-centered composite design. The influence of process parameters on five target responses, namely: total hydrolysable tannin (THT), color change (ΔE), maximum temperature evolved (Tmax), effective moisture diffusivity (Deff), and specific energy consumption (SEC) were analyzed. For microwave power and vacuum, the optimal conditions attained with aid of RSM and ANN were 260 W, 15.59 kPa, and 287 W, 19 kPa, respectively. The THT retention, ΔE, Tmax, Deff, SEC determined with RSM and ANN optimized values were 78.51 mg TAE/g of DW, 13.48, 67.02°C, 3.38 × 10−6 m2/s, 20.02 kWh/kg and 90.32 ± 0.35 mg TAE/g of DW, 12.488, 64.83°C, 5.64 × 10−6 m2/s, 20.01 ± 0.33 kWh/kg, respectively. The influence analysis explained that pomegranate peel dried at optimized ANN conditions of microwave power 287 W and vacuum 19 kPa possessed improved physicochemical properties and was efficient in terms of energy consumption compared to peel dried using RSM suggested conditions. Furthermore, the major criterion for evaluation between RSM and ANN tools in this study was the goodness of fit. The value for R2 produced via RSM varied between 0.8354 and 0.9870, whereas subsequent R2 in the case of ANN was considerably greater, varying between 0.9213 and 0.9956. The mean square error value displayed the minimum value in the case of ANN compared to the RSM model. These signified rather greater exactness and predictive capability of the ANN tool. Fourier transform infrared analysis established the existence of peaks between 3,308.92 and 908.89 cm−1 and high-performance liquid chromatography showed that pomegranate peel produced using microwave vacuum drying contains a good amount of gallic acid and tannic acid derivative compounds. Scanning electron microscopy elucidated that the combined microwave vacuum effect resulted in the formation of bigger pores on the peel surface ensuing in elevated extraction of THT at reduced energy consumption. Practical applications Shifting into a bio-economy is unrealistic with no proficient use of agricultural by-products and food waste. Microwave vacuum drying is a novel technique that would be applied to preserve the product bioactive components while drying. This study has established that drying of waste pomegranate peel using microwave vacuum dryer aided in excellent recovery rate of bioactive compounds particularly hydrolysable tannin (strong antioxidant and antimicrobial agent). This promises pomegranate peel powder obtained using microwave vacuum drying to be utilized in functional and nutraceutical food processing industries as a prospective resource of antimicrobial and antioxidant compounds. The findings also identified that the ANN-GA (artificial neural network–genetic algorithm) model would be effective for modeling the microwave vacuum drying method of pomegranate peel with preservation of peel quality. The optimal process values attained using coupled ANN-GA tool could be applied with respect to microwave vacuum-based industrial dryers to achieve the objective of drying at a considerably quicker rate.

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Artificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.

Does management graduates' emotional intelligence competencies predict their work performance? Insights from Artificial Neural Network Study

The study aims to predict the Perceived Work Performance of Management graduates through the application of Artificial Neural Network tool. It has examined the relationship between Emotional Quotient, Leadership Quotient and Work Experience of Management graduates through the use of Artificial Neural Network. The tool tested various competencies of the management graduates to predict the most important skill that can enable better work performance and reduce the gap of employability. The test was conducted among 33 management graduates which has assessed their Emotional Quotient Score, Personality trait and Leadership traits. A multilayer Artificial Neural Network tool was used to test the rank of competencies among EQ, Leadership Quality and work experience for the Perceived Work performance. The study has confirmed that Emotional Intelligence is a key factor that has distinguished from other factors like work experience, leadership quality and personality traits for Work Performance.

Experimental and artificial neural network approach for prediction of the thermal degradation behavior of sugarcane-based vulcanization additives in natural rubber compounds

The use of natural additives in elastomeric compounds is gaining the special attention of researchers and industry due to their potential applications as environmentally friendly compounds and lower cost-related. Another important issue is the use of powerful mathematical tools to predict experimental results, which is crucial for saving cost and time. Artificial neural network (ANN) combined with other mathematical methods, such as surface response methodology (SRM), can guarantee reliability and faster response of the predicted data for similar materials or properties. The great advantage of the present method is the fast prediction of the analyzed property, in the present case, thermal degradation curves, at heating rates not experimentally tested. In this study, a modified activator from sugarcane bagasse was incorporated in different concentrations in natural rubber compounds, and the degradation behavior was simulated by ANN and SRM based on the experimental thermal degradation curves at different heating rates from the thermogravimetric analysis. The simulated results showed an outstanding agreement with the experimental ones, evidencing the importance of using ANN and SRM tools in the prediction of properties of elastomeric compounds.

Elemental, Thermal and Physicochemical Investigation of Novel Biodiesel from Wodyetia Bifurcata and Its Properties Optimization using Artificial Neural Network (ANN)

In this study, an unexplored oil from the wodyetia bifurcata fruit was used for biodiesel production. The transesterification process was implemented to convert the raw oil into wodyetia bifurcata methyl ester (WBME) and the influence of process variables on WBME yield was examined with the response surface method (RSM) assisted Box-Behnken optimization. The results of RSM show that a maximum biodiesel yield of 94.67% was achieved and reaction time was identified as an influencing process variable. The fatty acid composition (FAC) from chromatography reveals the presence of highly unsaturated in WBME and the significant fuel properties of thermal and molecular meet the required fuel standards (ASTM). The obtained fuel properties of WBME are compared with other popularly used biodiesels and observed low kinematic viscosity (3.87mm2/sec) and moderated cetane number (53) for WBME. Furthermore, artificial neural network (ANN) tools are used for the prediction of WBME yield and show an improvement of 0.4% than RSM and low mean square error and a high coefficient of correlation was observed for ANN.

Adsorptive removal of dairy industrial pollutants by chitosan zinc –oxide nanoadsorbent- A comparitive study of artificial neural network and quadratic Box-Behnken design

Aim: To investigate the effect of operational parameters on the adsorption of biological oxygen demand (BOD) and chemical oxygen demand (COD) on to Chitosan zinc oxide (CZnO) nanoadsorbent using cost-effective and eco-friendly nanoadsorbent based effluent treatment processes. Methodology: CZnO nanoadsorbent particle was synthesized using chemical precipitation method. The nano size <100 nm was achieved using high-speed cryo all mill, followed by the characterization using high-end instruments such as scanning electron microscope with elemental detection sensor (SEM-EDS), atomic force microscope (AFM), X-ray diffractometer (XRD) and Fourier transform inform infrared spectroscopy (FT-IR). Modeling and optimization of operational parameters were done with the artificial neural network (ANN) and Box-BehnkenDesign (BBD) statistical tools. Results: Optimized treatment combination for adsorption of BOD and COD were found at initial BOD and COD concentration of 100 and 200 mg l−1, pH of 7.0 and 2.0, adsorbent dosage of 1.25 mg l−1, contact time of 100 and 60 min. In these conditionsthe desirability values of 0.988 and 0.950 were found for BOD and COD adsorption. The maximum per cent reduction of BOD and COD by using CZnO nanoadsorbent was found to be 96.71 and 87.56. Two models such as Quadratic Box-Behnken and ANN were compared in term of sum of square errors (SSE), root mean square error (RMSE) and correlation coefficient (R2) values. Interpretation: The results obtained revel the well trained ANN model found to be more accurate in prediction of BOD and COD adsorption process parameters compared to BBD model.