Optimisation of heat reflux extraction of gallic acid, quercetin and chlorogenic acid from Stevia rebaudiana (Bertoni) leaves by using response surface methodology and adaptive neuro-fuzzy inference system modelling

Assessment of adaptive neuro-fuzzy inference system and response surface methodology approaches in draft force prediction of subsoiling tines

Comparative analysis of RSM, ANN and ANFIS and the mechanistic modeling in eriochrome black-T dye adsorption using modified clay

Response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS) modeling was applied for optimization of process parameters for extraction of bioactive compounds from taro flour. A Face-centered central composite design (FCCD) was adopted to study the effects of process parameters on Total phenolic content (TPC), Total flavonoid content (TFC) and their antioxidant activity in terms of 1,1-Diphenyl-2-picryl-hydrazyl (DPPH), Ferric reducing antioxidant power (FRAP) and Total antioxidant activity (TAA) of taro flour. Three process parameters, solvent concentration, extraction temperature and extraction time were selected in range of 40–80% v/v, 30–60 °C and 1–5 h respectively. The optimum conditions for extraction of antioxidant from taro flour were obtained as solvent concentration 60% v/v, extraction temperature 48 °C and extraction time 4.0 h. Under these optimum conditions, the value of TPC, TFC, DPPH, FRAP and TAA were 14.17 mg GAE/100 g, 10.78 mg RE/100 g, 21.80%, 63.78 mg BHT Eq/100 g and 63.47 mg AAE/100 g with the desirability of 0.91. ANFIS model has been developed in terms of process parameters through Mamdani type fuzzy inference with triangular membership function for the prediction of responses using training data set. The results indicate that it rendered competitive results as obtained with RSM. Under the optimum conditions, experimental values were found close to the predicted values, obtained through RSM and ANFIS modeling. Liquid chromatography mass spectrometry (LCMS) analysis revealed that the caffeic acid, gallic acid, chlorogenic acid, catechin, epicatechin 3, 5 DiCQ acid and flavonols were the major compounds present in taro flour extract.

Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction

INTRODUCTION. Drugs based on rhizomes with roots of valerian (Valeriana officinalis L., family Valerianaceae) have sedative, antiarrhythmic, antispasmodic, anticonvulsant, anxiolytic effects and are widely used in psychoemotional and cognitive disorders, for the treatment of dysfunctions of the autonomic nervous system, including children, also for menopausal disorders. Biologically active compounds are contained not only in the underground, but also in the aboveground organs of the plant. This indicates the expediency of studying the qualitative chemical composition of valerian officinalis leaves to assess the possibility of use in medicine, subsequent standardization and the development of new phytopreparations based on them.AIM. Comparative phytochemical analysis of biologically active substances of leaves and rhizomes with roots of Valeriana officinalis by thin-layer chromatography.MATERIALS AND METHODS. Objects of research: leaves and rhizomes with roots of valerian harvested in various districts of the Minsk region of the Republic of Belarus in August 2023, dried by air-shade drying, ground to a coarse powder. The method of thin-layer chromatography on plates with a layer of silica gel (Sorbfil PTX-AF-A) was used to detect flavonoids and phenolic carboxylic acids. Chromatography was performed in the following mobile phases: ethyl acetate — formic acid anhydrous — water (8:1:1 and 10:2:3), acetic acid 2 and 15%; 0.020% solutions of rutin, quercetin, chlorogenic acid, and caffeic acid in 96% ethanol were used as comparison solutions. To detect sesquiterpene acids, chromatography was performed on plates with a layer of silica gel (Merck TLC Silica gel 60 F254) in the following mobile phases: glacial acetic acid — ethyl acetate — cyclohexane (2:38:60), ethyl acetate — hexane (10:90), acetone — hexane (1:2), hexane — ethyl acetate-glacial acetic acid (65:35:0.5). 0.025% solutions of valerenic acid and acetoxyvaleric acid in 96% ethanol were used as comparison solutions.RESULTS. The conditions of thin-layer chromatography have been selected to separate and identify the chemical components of leaves of Valeriana officinalis, as well as a comparative analysis with biologically active substances of rhizomes with roots of V. officinalis. It was revealed that the leaves of V. officinalis contain flavonoids, tannins and saponins, rhizomes with roots contain phenolic compounds, mainly tannins. The best separation of flavonoids and phenolic carboxylic acids was achieved in the solvent system formic acid anhydrous — water — ethyl acetate (1:1:8). It was found that extracts from the leaves of V. officinalis contain rutin and chlorogenic acid, do not contain quercetin and caffeic acid. The optimal mobile phase for the separation of sesquiterpenic acids from valerian leaf is the glacial acetic acid — ethyl acetate — cyclohexane (2:38:60) system.CONCLUSIONS. It has been established that alcoholic extracts of rhizomes with roots and leaves of valerian have a similar qualitative composition of flavonoids, phenolic carboxylic and sesquiterpenic acids. Rutin, chlorogenic, valerenic, and acetoxyvaleric acids were found in all samples. Thus, V. officinalis leaves can serve as a source of flavonoids, phenolic carboxylic and sesquiterpenic acids.

This research utilized a combined hydrodynamic cavitation reactor to produce biodiesel. The reactor worked automatically with the help of a controller designed by LabVIEW. For this purpose, rapeseed oil (0.5 L per experiment) and methanol alcohol with the sodium hydroxide catalyst were used for biodiesel production. The important factors of the study were: 1.pump flow rate (three levels of 1.4, 2 and 2.6 L/min); 2.the molar ratio of methanol to oil (4:1, 6:1 and 8:1); 3.the rotational speed of the reactor (8000, 12000 and 16000 rpm), and 4.circulation time (2, 4 and 6 min). The study analyzed the energy ratio (output energy/input energy) of the produced biodiesel to evaluate the system and modeled the performance of the system to obtain the best-operating conditions of the reactor. In this respect the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and response surface methodology (RSM) methods were employed. The average energy ratio was obtained 1.205, and the R2 of the best ANFIS, ANN and RSM models were 0.989, 0.966, and 0.990, respectively, and MSE was calculated at 0.0005, 0.0015 and 0.00003. The results revealed that the RSM and ANFIS models were preferred to the neural network model in terms of better performance, simplicity, and high processing speed. In general, the RSM model functioned better than the ANFIS model. Accordingly, the best reactor settings to obtain the maximum energy ratio (1.35) and biodiesel yield (91.87 %) was when the circulation time, the rotational speed, the pump flow rate and the molar ratio were set at 2 min, 8000 rpm, 1.4 L/min and 4, respectively.

Vegetable oils are a very important feedstock for many industries such as biofuels. There is the need to source for novel and underexploited plant oilseeds to meet the world demand for oils. Thus, the extraction of oil from Hura crepitans (sandbox) seeds was conducted using the solvent extraction method. Modeling of the extraction process was carried out using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The effects of the nature of the solvent (non-polar (n-hexane) and polar (acetone and ethyl acetate)), solid-solvent ratio (0.1–0.3 g/mL), extraction time (2–6 h), and their interactions on the oil yield were investigated using the D-optimal design technique. Performance assessment of the developed models was carried out to check their effectiveness in predicting the H. crepitans seed oil (HCSO) yield using various fit statistics. The coefficient of determination (R2) observed for the RSM and ANFIS models was 0.9720 and 0.9988, respectively, with corresponding mean relative percent deviation (MRPD) of 2.50 and 0.37%. Maximum HCSO yield of 62.95 wt% was achieved by ANFIS coupled with genetic algorithm (GA) using 0.1 g/mL solid-solvent ratio, extraction time of 4.19 h, and acetone, while maximum HCSO yield of 62.50 wt% was observed by RSM with a solid-solvent ratio of 0.1 g/mL, extraction time of 4.04 h, and acetone. Characteristics of the HCSO indicated that it could serve as a good feedstock for the production of oleochemicals such as biodiesel. The results obtained in this study demonstrated that ANFIS is marginally superior to RSM in the modeling of the HCSO extraction process, while GA was slightly better than the numerical tool of RSM in the optimization of the process.

In this paper, we compare and analyze the prediction performance of existing ANFIS (Adaptive Neuro Fuzzy Inference System) models. The ANFIS model is a fusion of neural networks with learning ability, adaptability, and computational ability, and a fuzzy system with human-like thinking ability and reasoning ability. In this paper, we analyze the prediction performance of ANFIS models using different input space partitioning methods. ANFIS 1 uses SC (Subtractive Clustering), a clustering method based on the density of data, to calculate the center of the cluster by calculating potential values inversely proportional to the distances of the different inputs. ANFIS 2 uses Fuzzy C-Means (FCM) clustering, which is a clustering method based on degree of belonging to data, and classifies each data belonging to each cluster according to degree of affiliation. ANFIS 3 is a method of using Context based Fuzzy C-Means (CFCM) clustering, which is a clustering method considering characteristics of input and output space based on FCM clustering. It creates clusters for each context generated according to characteristics of output space, to classify the data. To evaluate these ANFIS models, we use short term electricity price forecasting data and Boston housing data. The prediction performance is based on the Root Mean Squared Error (RMSE) method. Experimental results show that the prediction performance of the ANFIS model is different depending on the characteristics of each data. Furthermore, we confirmed that the prediction performance of ANFIS model using CFCM clustering method is constant.

The present study investigated the removal efficiency of Reactive Red 120 (RR120) using biochar produced from a seaweed Ulva reticulata using the predictive models namely Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The experiments were conducted in both batch and continuous processes, and the coded variables namely biochar dose, pH, Initial RR120 concentration, and temperature were studied for batch process, and the coded variables namely sorbent depth, solute flowrate, and initial RR120 concentration were studied for the continuous process. The correlation coefficient of the RSM and ANFIS for the batch process was obtained as 0.9977 and 0.9999. Similarly, for the continuous process, the correlation coefficient of 0.9979 and 0.9997 was obtained for RSM and ANFIS. Further statistical error analysis was conducted to find the goodness of the model with the experimental values. A comparison study was arrived based on the cluster analysis of experimental, RSM, and ANFIS models. The results concluded that the ANFIS model was superior to RSM in the prediction of the removal efficiency of the Reactive Red 120.

Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

Investigation of comparative machine learning models in effluent dephenolization process onto H3PO4-anchored corn cob

An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

With recent advances in Ambient Intelligence (AmI), it is becoming possible to provide support to a human in an AmI environment. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) model based scheme, named as prediction of the attention area using ANFIS (PAA_ANFIS), which predicts the human attention area on visual display with ordinary web camera. The PAA_ANFIS model was designed using trial and error based on various experiments in simulated gaming environment. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the prediction of eye-gaze area in the environment. PAA_ANFIS results show that ANFIS has been successfully implemented for predicting within different learning context scenarios in a simulated environment. The performance of the PAA_ANFIS model was evaluated using standard error measurements techniques. The Matlab® simulation results indicate that the performance of the ANFIS approach is valuable, accurate and easy to implement. The PAA_ANFIS results are based on analysis of different model settings in our environment. To further validate the PAA_ANFIS, forecasting results are then compared with linear regression. The comparative results show the superiority and higher accuracy achieved by applying the ANFIS, which is equipped with the capability of generating linear relationship and the fuzzy inference system in input-output data. However, it should be noted that an increase in the number of membership functions (MF) will increase the system response time.

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

Predictive capability evaluation of RSM, ANFIS and ANN: A case of reduction of high free fatty acid of palm kernel oil via esterification process