Prediction of heavy metal removal by different liner materials from landfill leachate: modeling of experimental results using artificial intelligence technique.

An intensive study has been made to see the performance of the different liner materials with bentonite on the removal efficiency of Cu(II) and Zn(II) from industrial leachate. An artificial neural network (ANN) was used to display the significant levels of the analyzed liner materials on the removal efficiency. The statistical analysis proves that the effect of natural zeolite was significant by a cubic spline model with a 99.93% removal efficiency. Optimization of liner materials was achieved by minimizing bentonite mixtures, which were costly, and maximizing Cu(II) and Zn(II) removal efficiency. The removal efficiencies were calculated as 45.07% and 48.19% for Cu(II) and Zn(II), respectively, when only bentonite was used as liner material. However, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Cu(II) removal (95%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (61.24% and 65.09%). Similarly, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Zn(II) removal (89.19%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (82.76% and 74.89%).

The design and implementation of adsorptive removal of Cu(II) from leachate using ANFIS.

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (2(3)), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 2(3) experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.

Study of montmorillonite clay for the removal of copper (II) by adsorption: full factorial design approach and cascade forward neural network.

An intensive study has been made of the removal efficiency of Cu(II) from industrial leachate by biosorption of montmorillonite. A 2(4) factorial design and cascade forward neural network (CFNN) were used to display the significant levels of the analyzed factors on the removal efficiency. The obtained model based on 2(4) factorial design was statistically tested using the well-known methods. The statistical analysis proves that the main effects of analyzed parameters were significant by an obtained linear model within a 95% confidence interval. The proposed CFNN model requires less experimental data and minimum calculations. Moreover, it is found to be cost-effective due to inherent advantages of its network structure. Optimization of the levels of the analyzed factors was achieved by minimizing adsorbent dosage and contact time, which were costly, and maximizing Cu(II) removal efficiency. The suggested optimum conditions are initial pH at 6, adsorbent dosage at 10 mg/L, and contact time at 10 min using raw montmorillonite with the Cu(II) removal of 80.7%. At the optimum values, removal efficiency was increased to 88.91% if the modified montmorillonite was used.

Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study.

PURPOSE: Artificial neural networks (ANNs) have been developed in the past few decades for many different applications in medical science and in biomedical research. The use of neural networks in oral and maxillofacial surgery is limited. The aim of this study was to determine the use of ANNs for the prediction of 2 subgroups of temporomandibular joint (TMJ) internal derangements (IDs) and normal joints using characteristic clinical signs and symptoms of the diseases. MATERIALS AND METHODS: Clinical symptoms and diagnoses of 161 patients with TMJ ID were considered the gold standard and were employed to train a neural network. After the training process, the symptoms and diagnoses of 58 new patients were used to verify the network's ability to diagnose. The diagnoses obtained from ANNs were compared with diagnoses of a surgeon experienced in temporomandibular disorders. The sensitivity and specificity of ANNs in predicting subtypes of TMJ ID were evaluated using clinical diagnosis as the gold standard. RESULTS: Eight cases evaluated as bilaterally normal in clinical examination were evaluated as normal by ANN. In detecting unilateral anterior disc displacement with reduction (ADDwR; clicking), the sensitivity and specificity of ANN were 80% and 95%, respectively. In detecting unilateral anterior disc displacement without reduction (ADDwoR; locking), the sensitivity and specificity of ANN were 69% and 91%, respectively. In detecting bilateral ADDwoR, the sensitivity and specificity of ANN were 37% and 100%, respectively. In detecting bilateral ADDwR, the sensitivity and specificity of ANN were 100% and 89%, respectively. In detecting cases of ADDwR at 1 side and ADDwoR at the other side, the sensitivity and specificity of ANN were 44% and 93%, respectively. CONCLUSION: The application of ANNs for diagnosis of subtypes of TMJ IDs may be a useful supportive diagnostic method, especially for dental practitioners. Further research, including advanced network models that use clinical data and radiographic images, is recommended.