Simulation and optimization of cheese whey additive for value-added compost production: Hyperparameter tuning approach and genetic algorithm.

Cheese whey is a difficult and costly wastewater to treat due to its high organic matter and mineral content. Although many management strategies are conducted for whey removal, its use in composting is limited. In this study, the effect of cheese whey in the composting of sewage sludge and poultry waste on compost quality and process efficiency was investigated.

Role of different natural materials in reducing nitrogen loss during industrial sludge composting: Modelling and optimization.

In this study, the effects of pumice, expanded perlite, and expanded vermiculite on nitrogen loss were examined for industrial sludge composting using the Box-Behnken experimental design. The independent factors and their levels were selected as amendment type, amendment ratio, and aeration rate, and codded as x, x, and x at 3 levels (low, center, and high). The statistical significance of independent variables and their interactions were determined at 95% confidence limits by Analysis of Variance.

Artificial intelligence and machine learning approaches in composting process: A review.

Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process.

Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm.

In this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.

Modeling and optimization of process parameters in co-composting of tea waste and food waste: Radial basis function neural networks and genetic algorithm.

In this study, the effects of co-composting of food waste (FW) and tea waste (TW) on the losses of total nitrogen (TN), total organic carbon (TOC), and moisture content (MC) were investigated. TW and FW were composted separately and compared with the co-composting of FW and TW at different ratios. While the MC losses were close to each other in all processes, the lowest TN and TOC losses were found in the composting process containing 25% TW as 26.

Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste.

The present study was carried out to improve, test, and validate the Cascade Forward Neural Network (CFNN) for co-composting of municipal solid waste (MSW) and cattle manure (CM). Composting was performed in vessel pilot-scale reactors with different CM rates for 105 days. The CFNN used 5 input variables containing CM and MSW mixture combinations, and 1 output for each of the compost quality parameters.

Identifying the maturity of co-compost of olive mill waste and natural mineral materials: Modelling via ANN and multi-objective optimization.

In this study, olive mill waste (OMW) and natural mineral amendments were co-composted to evaluate the compost maturity efficiency. The results were modelled by Feed-Forward Neural Networks (FF-NN) and Elman-Recurrent Neural Networks (ER-NN) and compared Response Surface Methodology (RSM). According to RSM produced a prediction error of more than 10% while Neural Networks (NNs) models were <2%.

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 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.

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.