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

Pilot-scale membrane-covered composting of food waste: Initial moisture, mature compost addition, aeration time and rate

The impact of different operational parameters on the composting efficiency and compost quality during pilot-scale membrane-covered composting (MCC) of food waste (FW) was evaluated. Four factors were assessed in an orthogonal experiment at three different levels: initial mixture moisture (IMM, 55 %, 60 %, and 65 %), aeration time (AT, 6, 9, and 12 h/d), aeration rate (AR, 0.2, 0.4, and 0.6 m3/h) and mature compost addition ratio (MC, 2 %, 4 %, and 6 %). Results indicated that 55 % IMM, 6 h/d AT, 0.4 m3/h AR, and 4 % MC addition ratio simultaneously provided the compost with the maximum cumulative temperature and the minimum moisture. It was shown that the IMM was the driving factor of this optimum composting process. On contrary, the optimal parameters for reducing carbon and nitrogen loss were 65 % IMM, 6 h/d AT, 0.4 m3/h AR, and 2 % MC addition ratio. The AR had the most influence on reducing carbon and nitrogen losses compared to all other factors. The optimal conditions for compost maturity were 55 % IMM, 9 h/d AT, 0.2 m3/h AR, and 6 % MC addition ratio. The primary element influencing the pH and electrical conductivity values was the AR, while the germination index was influenced by IMM. Protein was the main organic matter limiting the composting efficiency. The results of this study will provide guidance for the promotion and application of food waste MCC technology, and contribute to a better understanding of the mechanisms involved in MCC for organic solid waste treatment.

Enhancing the carbon content of coal gangue for composting through sludge amendment: A feasibility study

Cocomposting coal gangue and sludge eliminates the challenge of utilizing coal gangue. However, there is limited understanding about the feasibility of cocomposting sludge and coal gangue, as well as the composting indicators, functional microorganisms, and safety risks involved. Therefore, this study evaluated the feasibility of enhancing carbon composting in coal gangue by incorporating sludge along with sawdust as a conditioner. Three laboratory-scale reactors were designed and labeled as T1 (20 % coal gangue, 60 % sludge, and 20 % sawdust), T2 (40 % coal gangue, 40 % sludge, and 20 % sawdust), and T3 (60 % coal gangue, 20 % sludge, and 20 % sawdust). Seed germination and plant growth assessments were conducted to ensure compost stability and assess phytotoxicity to cabbage (Brassica rapa chinensis L.) in terms of growth and biomass. The results indicated that the temperature, pH, EC and ammonia nitrogen of all three reactor conditions met the requirements for product decomposition. Composting was successfully achieved when the sludge proportion was 20 % (T3). However, when the sludge proportion was markedly high (T1), the harmlessness of the compost was reduced. The germination indices of T1, T2, and T3 reached 95 %, 122 %, and 119 % at maturity, respectively. This confirmed that the harmless cycle, which involved promoting condensation and aromatization, enhancing decay, and reducing composting time, was shorter in T2 and T3 than in T1. Coal gangue can also serve as a beneficial habitat for microorganisms, promoting an increase in their population and activity. Potting experiments in sandy soil revealed that the mechanism of action of compost products in soil included not only the enhancement of soil nutrients but also the improvement of soil texture. The results of this study suggest that using coal gangue as a raw material for composting is an efficient and environmentally friendly approach for producing organic fertilizers.

Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges

Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simultaneously producing organic fertilizers and soil amendments. This biologically-driven biochemical reaction process, however, presents challenges due to its complex non-linear metabolism and the heterogeneous nature of the solid medium. These characteristics inherently limit the simulation accuracy and efficiency optimization in aerobic composting. Recently, significant efforts have been made to simulate and control composting process parameters, as well as predicting and optimizing composting product quality. Notably, the integration of machine learning (ML) in aerobic composting of organic waste has garnered considerable attention for its applicability and predictive capability in exploring the complex non-linear relationships of organic waste composting parameters. Despite numerous studies on ML applications in OSW composting, a systematic review of research findings in this field is lacking. This study offers a systematic overview of the application level, current status, and versatility of ML in OSW composting. It spans various aspects, such as compost maturity, environmental pollutants, nutrients, moisture, heat loss, and microbial metabolism. The survey reveals that ML-intervention predominantly focuses on compost maturity and environmental pollutants, followed by nutrients, moisture, heat loss, and microbial activity. The most commonly employed predictive models and optimization algorithms are artificial neural networks (47%) and genetic algorithms (10%). These demonstrate high prediction accuracy and maximize composting efficiency in the simulation and prediction of organic waste composting, alongside regulation of key parameters. Deep neural networks and ensemble learning models prove effective in achieving superior predictive performance by selecting feature variables in compost maturity and pollutant residue prediction of organic waste composting in a simpler and more objective manner.

Double ionic liquid reinforced g-CN nanocomposite for an enhanced adsorption of methylparaben: Mechanism, modeling, and optimization

The efficient removal of organic pollutants, especially pharmaceuticals, from aquatic environments has attracted great attentions. Application of green, multipurpose, and inexpensive compounds is being extensively favorite as adsorbent instead of the traditional chemicals or materials. In this study, sulfonated graphitic carbon nitride was modified with two ionic liquids of polyethyleneimine and choline chloride to create a novel nanocomposite (Sg-CN@IL2 NC) and to use for removal of methylparaben (MeP) from aqueous media. After confirmation of the successful synthesized using different methods, the effective parameters for MeP removal, such as initial MeP concentration, adsorbent dose, sonication time, and temperature, as well as their interactions, were experimentally examined and modeled using response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The models were then optimized using desirability function analysis (DF) and genetic algorithm (GA). The results showed that MeP adsorption: a) can be explained more accurate and reliable using GRNN (AARD% = 11.67, MAE = 15.31, RAE % = 45.42, RRSE % = 55.18, MSE = 435.86, RMSE = 20.70, and R2 = 0.995) than the others; b) reached equilibrium within 7.0 min with a maximum uptake of 267.2 mg/g at a temperature of 45 °C and a neutral pH; c) followed from Freundlich (R2 = 0.999) isotherm and PSO kinetic (R2 = 0.95) models; d) is endothermic and spontaneous; e) is mainly due to π-π stacking, electrostatic and hydrogen bonding interactions. Moreover, Sg-CN@IL2 NC showed an appropriate reusability for up to five cycles. These findings demonstrate the potential of as-prepared NC as an excellent adsorbent for removal of MeP from aqueous media.

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value. The four models were compared, and the Extra Trees algorithm exhibited the highest prediction accuracy with R2 values of 0.928 for GI and 0.957 for T value. To identify the interactions between critical parameters and compost maturity, The Pearson correlation matrix and Shapley Additive exPlanations (SHAP) analysis were conducted. Furthermore, the accuracy of the models was validated through compost validation experiments. These findings highlight the potential of applying machine learning algorithms to predict green waste compost maturity and optimise process regulation.

Optimization of olive oil extraction wastes co composting procedure based on bioprocessing parameters

Organic waste generation has increased massively around the world during the last decades, especially the waste produced by the olive-growing industry. In order to manage the waste accumulation, composting process is an appropriate biotechnological solution which allows the waste organic matter biotransformation into a useful product the "compost", used as an amendment for agricultural soils. The classical composting process presents several disadvantages; the major difficulty is to find the best feedstocks proportion to be used, leading to a final C/N ratio ranged between 12 and 15, a neutral pH, a humidity between 40% and 60% and organic matter (OM) content of 20-60%, at ambient temperature. Consequently, an accurate optimization of the composting process is needed for predicting the process parameters progress. To optimize these parameters and the waste rates initially mixed, the multiple regression method was used to determine the compost final parameters values, referring to the initial mixture of the different waste types. The best model filling the required standardized values included 49% of olive mill wastewater, 19.5% of exhausted olive mill cake, 15.5% of poultry manure, and 16% of green waste. This combination provides a pH of 7.5, a C/N ratio of 12.5 and an OM content of 44%. Such modelization would enshorten the composting required time.

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 x₁, x₂, and x₃ 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. The quadratic polynomial regression equation produced to predict the responses was solved and the optimum values of the variables were predicted by analyzing the three-dimensional response surfaces plots. The optimum conditions for minimum nitrogen loss by the regression model were as pumice of amendment type, 40% of amendment ratio, and 6 L/min of aeration rate. In this study, it was observed that time-consuming and laborious laboratory work can be minimized with the Box-Behnken experimental design.

Artificial neural network model- and response surface methodology-based optimization of Atractylodis Macrocephalae Rhizoma polysaccharide extraction, kinetic modelling and structural characterization

Atractylodis Macrocephalae Rhizoma (AMR) is the dried rhizome of Atractylodes macrocephala Koidz, which is widely used in the development of health products. AMR contains a large number of polysaccharides, but at present there are fewer applications for these polysaccharides. In this study, the effects of different extraction methods on the Atractylodis Macrocephalae Rhizoma polysaccharide (AMRP) yield were investigated, and the conditions for ultrasound-assisted extraction were optimized by response surface methodology (RSM) and three neural network models (BP neural network, GA-BP neural network and ACO-GA-BP neural network). The best conditions were a liquid-to-solid ratio of 17 mL/g, ultrasonic power of 400 W, extraction temperature of 72 °C, and extraction time of 40 min, which yielded 31.31% AMRP. The kinetic equation of AMRP was determined and compared with the results predicted by three neural network models. It was finally determined that the extraction conditions, kinetic processes and kinetic equation predicted by the GA-ACO-BP neural network were optimal. In addition, AMRP was characterized using SEM, FTIR, HPLC, UV, XRD, and NMR, and the structural study revealed that AMRP has a rough exterior and a porous interior; moreover, it contains high levels of glucose (5.07%), arabinose (0.80%), and galactose (0.74%). AMRP has three crystal structures, consisting of two β-type monosaccharides and one α-type monosaccharide. Additionally, the effectiveness of AMRP as an antioxidant was demonstrated in an in vitro experiment.

Evaluation of the influence of rice husk amendment on compost quality in the composting of sewage sludge

This study aimed to evaluate the influence of rice husk addition on compost quality and maturity in sewage sludge composting using a pilot scale aerated in-vessel reactor. During the composting process, changes in compost quality and physicochemical factors including pH, temperature, moisture content, electrical conductivity, total organic carbon (TOC), total nitrogen (TN), and carbon to nitrogen ratio (C/N) were monitored. In the pile containing 25% rice husk, the lowest losses occurred with 52.49% for TOC and 23.24% for TN, while C/N ratio in the final compost was 18.82, achieving mature and quality compost. The moisture contents of the final composts were found as 50.72% in the control group while it was 31.73% and 28.18% in the reactors containing 10% and 25% rice husk, respectively. These results suggested that rice husk addition was beneficial for reducing moisture content and balancing the C/N ratio in sewage sludge composting.

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. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.

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 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.