Prediction compost criteria of organic wastes with Biochar additive in in-vessel composting machine using ANFIS and ANN methods

Abdi, Roozbeh; Shahgholi, Gholamhossein; Sharabiani, Vali Rasooli; Fanaei, Adel Rezvanivand; Szymanek, M.

doi:10.1016/j.egyr.2023.01.001

Cited by 13 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multilayer perceptron (MLP) neural networks were developed to further improve the prediction of physiochemical properties of grape-skin compost during the composting process. ANNs were compared to PLR modelling as nonlinear models, and it was expected that the ANNs could better and more accurately describe the experimental data [ 56 ]. ANN models were developed individually for each of the selected physicochemical properties of the compost.…”

Section: Resultsmentioning

confidence: 99%

“…The results obtained are in agreement with the results presented by Hosseinzadeh et al [ 58 ] in which the ANN models provided a better prediction for the recovery of total nitrogen and total phosphorus from waste by vermicomposting than the MLR models. Furthermore, the superior prediction performance of ANN modelling compared to multiple linear regression was also shown by: (i) Dumenci et al [ 59 ] for evaluation of olive mill waste compost based on composting mixture composition, (ii) Singh et al [ 60 ] for modeming of compost production under different climate conditions, (iii) by Shi et al [ 61 ], for prediction of humic acid content in the final compost based on the carbon-to-nitrogen content, initial moisture content, type of inoculant and composting day and (iv) Abdi et al [ 56 ] for prediction of electrical conductivity, pH, carbon-to-nitrogen ratio and germination index, based on inlet-air rates, initial carbon-to-nitrogen ratios of 18 and the addition of coco peat biochar.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

Sokač Cvetnić,

Krog,

Valinger

et al. 2024

Bioengineering

View full text Add to dashboard Cite

The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost’s physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

Sokač Cvetnić,

Krog,

Valinger

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…TN peaked on day 7 in T1, T2, and T4, and on day 14 in T3. A previous study [49] found that treatment with biochar and BV significantly reduced TN loss. The decreased TN loss by BV might be due to the high content of organic acid in BV, which neutralizes ammonia [20].…”

Section: Variation In Total Nitrogen (Tn) and Tn Loss During Compostingmentioning

confidence: 89%

“…EC values of the final compost ranged from 3.3 to 3.6 mS•cm −1 . These changes in EC values may be attributed to the decomposition of organic matter, as well as the loss of weight and mineralization that occur during composting, which can contribute to a higher EC [49].…”

Section: Variations In Moisture Content During Compostingmentioning

confidence: 99%

Effect of Bamboo Vinegar on Control of Nitrogen Loss in Vegetable Waste and Manure Composting

Liu

Lin

et al. 2023

Agriculture

View full text Add to dashboard Cite

The large-scale generation of vegetable waste in China has become a significant environmental concern. The traditional method of composting results in high nitrogen losses during the process and in the final product. To address this issue and shorten the composting period, this study investigated the effects of bamboo vinegar (BV) and a microbial inoculant (MI) on the physical and chemical properties of the compost and bacterial community composition during the composting process. The results revealed that the addition of BV and BV + MI decreased the time required to reach thermophilic temperatures and conserved nitrogen in the final product. Furthermore, it was found that the dominant nitrifying and denitrifying bacteria, as identified through 16S rDNA analysis, belonged to Nitrosomonas and Proteobacteria, respectively. BV and BV + MI reduced NH3 and N2O emissions, which suggested that BV is a beneficial composting agent that preserves nitrogen during the composting process.

show abstract

“…They calculated the highest exergy destruction and the lowest exergy efficiency for the first evaporator (Bapat et al, 2016). Recently, some innovative studies were conducted related to the various processes in the field of foods and industrial applications, so these fields still need more detailed investigations using new methods (Abdi et al, 2023;Aghaei et al, 2022;Aghdamifar et al, 2023;Akhoundzadeh Yamchi et al, 2024;Dizajyekan et al, 2021Dizajyekan et al, , 2022Emami et al, 2024;Mohammadzadeh et al, 2021Mohammadzadeh et al, , 2022Rezvanivand Fanaei et al, 2021;Rezvanivand Fanayi & Nikbakht, 2015;Zobeiri et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A comparative evaluation of energetic and exergetic performance parameters in syrup purification and evaporation lines

Piri,

Fanaei,

Rostampour

2024

J Food Process Engineering

Self Cite

View full text Add to dashboard Cite

Considering the global challenges of environmental destruction, limited fossil reserves, and the reduction of the share of energy in the final price of the product, a detailed evaluation of energy‐intensive systems is needed to improve performance. The syrup heating process is one of the basic processes in sugar factories. In this study, the syrup heating system in the purification and evaporation lines, located in the Urmia sugar plant, was evaluated, analyzed, and compared with energy and exergy analysis. Also, the subsystems of the syrup heating process were evaluated with thermodynamic analysis. Mass, energy, and exergy balances were carried out by coding in EES software, and energetic and exergetic operational parameters were extracted. The heat loss rate and exergy destruction rate in the syrup heating system of the purification line were calculated as 121.29 and 404.36 kW, respectively, and in the syrup heating system of the evaporation line as 112.70 and 238.62 kW, respectively. The recovery potential rate was 134.99 kW for the purification line and 37.70 kW for the evaporation line. The stability index and exergy efficiency for the purification line were calculated as 3.03% and 67.01% for the purification line and 6.54% and 84.71% for the evaporation line. The results of the thermodynamic analysis showed that the syrup heating process in the purification line has weaker performance from the energetic and exergetic point of view, is accompanied by large losses, and imposes a greater environmental burden. Accordingly, it is suggested that the improvement of the syrup heating performance in the purification line should be prioritized for engineering optimization and redesign in sugar processing plants.Practical applicationsMonitoring the operational state of the systems and determining system losses are critical steps in food thermal processing. A practical and accurate method to evaluate the purification and evaporation lines is required to improve the thermal efficiency. According to exergy analyses, the performance of the syrup heating process in the purification line is low due to large losses and imposes a greater environmental burden. Therefore, the improvement of the syrup heating performance in the purification line should be prioritized for engineering optimization and redesign in sugar processing plants.

show abstract

Prediction compost criteria of organic wastes with Biochar additive in in-vessel composting machine using ANFIS and ANN methods

Cited by 13 publications

References 34 publications

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

Effect of Bamboo Vinegar on Control of Nitrogen Loss in Vegetable Waste and Manure Composting

A comparative evaluation of energetic and exergetic performance parameters in syrup purification and evaporation lines

Contact Info

Product

Resources

About