# Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{*}

^{†}

## Abstract

**:**

^{2}of 0.94 and RMSE of 2.62.

## 1. Introduction

_{2}atmosphere were systematically investigated. In their experiment, the co-HTC of SD and SS was demonstrated to be an effective primary treatment of gasification toward water and gas generation of outstanding quality [23]. Ismail et al. designed an ANN–Kriging architecture for prognosticating the recovery of inorganic phosphorus and carbon from HTC [24]. Experiments with poultry litter on the recovery of C and IP from HTC for various settings were obtained for this system (time and /2 temperature). Kriging interpolation was utilized to create a limited amount of extra sample points for gathered trial scores to develop an ANN model for this model with increased precision. An enhanced ANN algorithm was used to create a group of exponential equations. These equations forecasted CR and IP based on process factors and then generated a system of LE, which was utilized in the further dynamic optimization of the HTC process. Unlike ANN, the SVM technique is predicated on the fundamental risk mitigation approach, aiming to reduce the UB of misclassifications rather than the empirical error. SVM seems to have a distinct benefit over ANN in that it could be conceptually studied using principles from statistical ML theory. While we tended to discover a universal result in the course of training, we encountered structural risk in complex models while training SVM. To obtain local minima, the ANN applies the gradient descent learning technique. Consequently, overfitting occurs frequently, particularly for challenging nonlinear processes. We therefore incorporated E-SVM in our suggested model to avoid this issue [25]. Ensemble-SVM provides users with quick access to tools for experimenting with SVM ensembles. Training ensemble models are substantially faster than training ordinary LIBSVM models with a comparable prediction accuracy. Due to their low training complexity, linear algorithms are commonly used in large-scale learning.

## 2. Materials and Methods

#### 2.1. Dataset and Preprocessing

#### 2.2. Data Preprocessing

_{i}represents the value of the input feature i, ${x}_{i}^{*}$ represents the normalized value of x

_{i}, and s and u represent the standard deviation and mean PCC (Pearson-correlation-coefficient) for x

_{i}, respectively, which were was used to acquiring a quick grasp for the correlation among the input variables and outcome goals. Then, P

_{xy}was obtained using Equation (2), which is the PCC value for target–target/feature–target.

_{xy}was between −1 and +1. Additionally, zero represents a non-linear correlation [11].

#### 2.3. Ensemble-SVM for Classification

_{i}(x) is incorrect, almost all the other classifiers could be accurate. Then, the outcome of a majority of votes could be trusted. If p < ½ for a specific classifier, then the probability, p

_{E}, that the popular vote outcome is erroneous is ${\sum}_{k=\left[\frac{n}{2}\right]}^{n}({p}^{k}{\left(1-p\right)}^{\left(n-k\right)}.{\sum}_{k=\left[\frac{n}{2}\right]}^{n}{\left(\frac{1}{2}\right)}^{k}{\left(\frac{1}{2}\right)}^{\left(n-k\right)}={\sum}_{k=\left[\frac{n}{2}\right]}^{n}{\left(\frac{1}{2}\right)}^{n}$. The probability, ${p}_{E},$ decreases as the number of classifiers (n) increases. SVM is noted for its high generalization efficiency and ease of learning precise global optimal parameters. For these benefits, its ensemble would not be regarded as a viable strategy for significantly boosting the classifier’s performance. However, practical SVMs use approximation techniques to minimize the computing complexities of duration and storage. An individual SVM might not even be capable of learning the accurate variables of the global optimum. Occasionally, support vectors acquired during training are insufficient to classify every unfamiliar test sample adequately.

#### 2.4. Slime Mould Optimization

#### 2.4.1. Approaching Food Algorithm

_{b}constitutes the present, single location with respect to a high concentration of odor; t shows the present iteration; X represents the slime mold’s locality; X

_{A}and X

_{B}are arbitrarily picked singulars from the mold; and W denotes the weight of the slime mold. The equation for p can be written as: p = tanh[S(i) – DF], where i $\in $ 1, 2, 3, …, n, and S(i) represents the fitness of $\overrightarrow{X}$ [30]. As previously described, a varies from −a to a, in which a can be written as:

#### 2.4.2. Warp Food Algorithm

#### 2.5. Training and Evaluation

_{total}) is,

_{1}, w

_{2}… w

_{m}] = W; the vector matrix (w

_{t}) of the hypothesis function is trained by L

_{total}; λ

_{t}is the scalar coefficient, which evaluates importance of various loss objectives, for which each of these projects is set to be the same; L

_{t}represents the ML model’s task (t) loss function; and x

^{t}

_{i}and y

^{t}

_{i}are the input and output values of the task t, respectively. The results of the proposed model were evaluated based on the test data’s RMSE [32] and regression coefficient (R

^{2}) for which Equations (8)–(11) were calculated to evaluate the performance of the model [33]. The mathematical formulae are mentioned below:

^{t}

_{pred.i}represents the predicted value of target t; Y

^{t}

_{exp,i}denotes the target’s experimental data; and n shows the amount of data [34].

#### 2.6. Analysis Method for Feature Importance

## 3. Results and Discussion

#### 3.1. Statistical Analysis of Sewage Sludge, Food Waste, Cattle Manure, and Hydrochar Characteristics

#### 3.2. ML Model’s Hyperparameter Tuning and Variable Correlation

#### 3.3. Evaluation of ML Model’s Optimization for Testing Dataset

^{2}and RMSE. If the RMSE value was low or the R

^{2}value was closer to 1.0, then the prediction accuracy would be high. For diagnosing ER, C char, H/C, and CR, the R

^{2}multi-task of RF was higher than the single-task R

^{2}, as can be seen in Figure 5, but the latter remained significantly greater with respect to predicting targets. The RF models, on the other hand, performed poorly (Figure 6a). Although the efficiency for predicting a single task with an RF might be enhanced by setting hyperparameters for every function individually, it is laborious and costly to implement. All the R

^{2}values of the multi-task prediction of the optimized Ensemble SVM algorithm were over 0.85. In addition, the mean R

^{2}was 0.90, which is much greater than compared to the R

^{2}for predicting a task. With multi-task prediction, both the SVM and RF models achieved a minimum average RMSE (Figure 6b). When comparing the suggested model to the existing model shown in Table 2, Ensemble SVM ranked among the top because it had the lowest RMSE and the highest R

^{2}.

#### 3.4. Slime Mould Algorithm Optimization of Hydrochar Properties Based on Ensemble SVM

## 4. Conclusions

^{2}of 0.94 and an RMSE of 2.62. In addition, the yield of the hydrochar is mainly affected by the HC conditions, especially temperature and water content. In contrast, the carbon and ash contents of sewage sludge, food waste, and animal manure were major contributors to the HHV and C-char. Biofuel ratios and functional states, especially elemental content and temperature, appear to be critical for predicting HC CSS and FP efficiency based on an ML-constructed feature analysis. The ensemble-ML-based slime mold model produced non-inferior solutions with input constraints for optimizing the CCS/FP HC for different applications. The unique importance of Pareto solutions and brackets can provide direction for the fabrication of ideal hydrochar while saving labor, time, and money. In an effort to further reduce numerous hazardous emissions, MSW recycling and energy recovery should be precisely predicted with experimental validation that is carried out using an optimized deep learning.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Multi-Task Prediction of Fuel Properties of Hydrochar Derived from Wet Municipal Wastes with Random Forest. Available online: https://www.researchgate.net/profile/jie-li-85/publication/343124219_multi-task_prediction_of_fuel_properties_of_hydrochar_derived_from_wet_municipal_wastes_with_random_forest/links/5f1799dda6fdcc9626a67c5a/multi-task-prediction-of-fuel-properties-of-hydrochar-derived-from-wet-municipal-wastes-with-random-forest.pdf (accessed on 20 June 2022).
- Birgen, C.; Magnanelli, E.; Carlsson, P.; Skreiberg, Ø.; Mosby, J.; Becidan, M. Machine learning based modelling for lower-Ju heating value prediction of municipal solid waste. Fuel
**2020**, 283, 118906. [Google Scholar] [CrossRef] - AlZubi, A.A. IoT-based automated water pollution treatment using machine learning classifiers. Environ. Technol.
**2022**, 1–9. [Google Scholar] [CrossRef] [PubMed] - Li, H.; Liu, X.; Legros, R.; Bi, X.T.; Lim, C.J.; Sokhansanj, S. Pelletization of torrefied sawdust and properties of torrefied pellets. Appl. Energy
**2012**, 93, 680–685. [Google Scholar] [CrossRef] - Liu, Z.; Quek, A.; Balasubramanian, R. Preparation and characterization of fuel pellets from woody biomass, agro-residues and their corresponding hydrochars. Appl. Energy
**2014**, 113, 1315–1322. [Google Scholar] [CrossRef] - Xie, S.; Yu, G.; Li, C.; You, F.; Li, J.; Tian, R.; Wang, G.; Wang, Y. Dewaterability enhancement and heavy metals immobilization by pig manure biochar addition during hydrothermal treatment of sewage sludge. Environ. Sci. Pollut. Res.
**2019**, 26, 16537–16547. [Google Scholar] [CrossRef] - Diggelman, C.; Ham, R.K. Household food waste to wastewater or to solid waste? That is the question. Waste Manag. Res. J. Sustain. Circ. Econ.
**2003**, 21, 501–514. [Google Scholar] [CrossRef] - Leng, L.; Yuan, X.; Shao, J.; Huang, H.; Wang, H.; Li, H.; Chen, X.; Zeng, G. Study on demetalization of sewage sludge by sequential extraction before liquefaction for the production of cleaner bio-oil and bio-char. Bioresour. Technol.
**2016**, 200, 320–327. [Google Scholar] [CrossRef] - Mau, V.; Gross, A. Energy conversion and gas emissions from production and combustion of poultry-litter-derived hydrochar and biochar. Appl. Energy
**2018**, 213, 510–519. [Google Scholar] [CrossRef] - Tian, H.; Li, J.; Yan, M.; Tong, Y.W.; Wang, C.-H.; Wang, X. Organic waste to biohydrogen: A critical review from technological development and environmental impact analysis perspective. Appl. Energy
**2019**, 256, 113961. [Google Scholar] [CrossRef] - Li, J.; Zhu, X.; Li, Y.; Tong, Y.W.; Ok, Y.S.; Wang, X. Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource. J. Clean. Prod.
**2020**, 278, 123928. [Google Scholar] [CrossRef] - Hameed, Z.; Aslam, M.; Khan, Z.; Maqsood, K.; Atabani, A.; Ghauri, M.; Khurram, M.S.; Rehan, M.; Nizami, A.-S. Gasification of municipal solid waste blends with biomass for energy production and resources recovery: Current status, hybrid technologies and innovative prospects. Renew. Sustain. Energy Rev.
**2020**, 136, 110375. [Google Scholar] [CrossRef] - Qian, X.; Lee, S.; Chandrasekaran, R.; Yang, Y.; Caballes, M.; Alamu, O.; Chen, G. Electricity Evaluation and Emission Characteristics of Poultry Litter Co-Combustion Process. Appl. Sci.
**2019**, 9, 4116. [Google Scholar] [CrossRef][Green Version] - Tasca, A.L.; Puccini, M.; Gori, R.; Corsi, I.; Galletti, A.M.R.; Vitolo, S. Hydrothermal carbonization of sewage sludge: A critical analysis of process severity, hydrochar properties and environmental implications. Waste Manag.
**2019**, 93, 1–13. [Google Scholar] [CrossRef] [PubMed] - Li, L.; Flora, J.R.; Caicedo, J.M.; Berge, N.D. Investigating the role of feedstock properties and process conditions on products formed during the hydrothermal carbonization of organics using regression techniques. Bioresour. Technol.
**2015**, 187, 263–274. [Google Scholar] [CrossRef] [PubMed] - Cao, H.; Xin, Y.; Yuan, Q. Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach. Bioresour. Technol.
**2016**, 202, 158–164. [Google Scholar] [CrossRef] - Taki, M.; Rohani, A. Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation. Case Stud. Therm. Eng.
**2022**, 31, 101823. [Google Scholar] [CrossRef] - Jassim, M.S.; Coskuner, G.; Zontul, M. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Manag. Res. J. Sustain. Circ. Econ.
**2021**, 40, 195–204. [Google Scholar] [CrossRef] - Adeleke, O.; Akinlabi, S.; Jen, T.-C.; Dunmade, I. A machine learning approach for investigating the impact of seasonal variation on physical composition of municipal solid waste. J. Reliab. Intell. Environ.
**2022**, 1–20. [Google Scholar] [CrossRef] - Riaz, A.R.; Gilani, S.M.M.; Naseer, S.; Alshmrany, S.; Shafiq, M.; Choi, J.G. Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture. Sustainability
**2022**, 14, 10964. [Google Scholar] [CrossRef] - Ro, K.S.; Flora, J.R.V.; Bae, S.; Libra, J.A.; Berge, N.D.; Álvarez-Murillo, A.; Li, L. Properties of Animal-Manure-Based Hydrochars and Predictions Using Published Models. ACS Sustain. Chem. Eng.
**2017**, 5, 7317–7324. [Google Scholar] [CrossRef] - Li, J.; Pan, L.; Suvarna, M.; Tong, Y.W.; Wang, X. Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning. Appl. Energy
**2020**, 269, 115166. [Google Scholar] [CrossRef] - Ma, J.; Chen, M.; Yang, T.; Liu, Z.; Jiao, W.; Li, D.; Gai, C. Gasification performance of the hydrochar derived from co-hydrothermal carbonization of sewage sludge and sawdust. Energy
**2019**, 173, 732–739. [Google Scholar] [CrossRef] - Ismail, H.Y.; Shirazian, S.; Skoretska, I.; Mynko, O.; Ghanim, B.; Leahy, J.J.; Walker, G.M.; Kwapinski, W. ANN-Kriging hybrid model for predicting carbon and inorganic phosphorus recovery in hydrothermal carbonization. Waste Manag.
**2019**, 85, 242–252. [Google Scholar] [CrossRef] [PubMed] - Bokhari, S.A.; Saqib, Z.; Amir, S.; Naseer, S.; Shafiq, M.; Ali, A.; Zaman-ul-Haq, M.; Irshad, A.; Hamam, H. Assessing land cover transformation for urban environmental sustainability through satellite sensing. Sustainability
**2022**, 14, 2810. [Google Scholar] [CrossRef] - Kim, H.-C.; Pang, S.; Je, H.-M.; Kim, D.; Bang, S.Y. Constructing support vector machine ensemble. Pattern Recognit.
**2003**, 36, 2757–2767. [Google Scholar] [CrossRef] - Li, S.; Chen, H.; Wang, M.; Heidari, A.A.; Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. Future Gener. Comput. Syst.
**2020**, 111, 300–323. [Google Scholar] [CrossRef] - Patino-Ramirez, F.; Boussard, A.; Arson, C.; Dussutour, A. Substrate composition directs slime molds behavior. Sci. Rep.
**2019**, 9, 15444 . [Google Scholar] [CrossRef][Green Version] - Zubaidi, S.L.; Abdulkareem, I.H.; Hashim, K.S.; Al-Bugharbee, H.; Ridha, H.M.; Gharghan, S.K.; Al-Qaim, F.F.; Muradov, M.; Kot, P.; Al-Khaddar, R. Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand. Water
**2020**, 12, 2692. [Google Scholar] [CrossRef] - Kumar, C.; Raj, T.D.; Premkumar, M. A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik
**2020**, 223, 165277. [Google Scholar] [CrossRef] - Mostafa, M.; Rezk, H.; Aly, M.; Ahmed, E.M. A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel. Sustain. Energy Technol. Assess.
**2020**, 42, 100849. [Google Scholar] [CrossRef] - Yin, C.-Y. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel
**2010**, 90, 1128–1132. [Google Scholar] [CrossRef][Green Version] - Qian, X.; Lee, S.; Soto, A.-M.; Chen, G. Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis. Resources
**2018**, 7, 39. [Google Scholar] [CrossRef][Green Version] - Zhu, X.; Li, Y.; Wang, X. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresour. Technol.
**2019**, 288, 121527. [Google Scholar] [CrossRef] - Wu, G.; Kechavarzi, C.; Li, X.; Wu, S.; Pollard, S.J.; Sui, H.; Coulon, F. Machine learning models for predicting PAHs bioavailability in compost amended soils. Chem. Eng. J.
**2013**, 223, 747–754. [Google Scholar] [CrossRef] - Jiang, W.; Xing, X.; Li, S.; Zhang, X.; Wang, W. Synthesis, characterization and machine learning based performance prediction of straw activated carbon. J. Clean. Prod.
**2018**, 212, 1210–1223. [Google Scholar] [CrossRef] - Cheng, F.; Porter, M.D.; Colosi, L.M. Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology? Energy Convers. Manag.
**2020**, 203, 112252. [Google Scholar] [CrossRef]

**Figure 3.**Volin plot for statistical data of elementary composition (

**a**,

**b**); proximate analysis (

**c**,

**d**) of waste with high moisture content, and HTC conditions (

**e**,

**f**).

**Figure 4.**Data statistics in violin plot: (

**a**) yield of hydrochar, (

**b**) CC and CR of hydrochar, and (

**c**) HHV and ER of hydrochar.

**Figure 5.**The PCC method was employed for exploring features–targets and targets–targets (248 types of data was analyzed for C_char, T, A, t, CR, Fc, WC, V, ER, and HHV).

**Figure 6.**Prediction accuracy for the eight targets in the two distinct optimized models of (

**a**) the average R

^{2}and (

**b**) average RMSE (RF, ESVM).

**Figure 7.**Pareto curves of a hydrochar generated from sludge, food waste, and animal dung: (

**a**) HHV vs. ER and (

**b**) C Char vs. CSS.

**Table 1.**LB and UB of variables for various types of wastes, their elemental composition, proximal composition, and operational condition.

Content | Sewage Sludge | Food Waste | Cattle Manure | ||||
---|---|---|---|---|---|---|---|

LB | UB | LB | UB | LB | UB | ||

Elemental Composition | C (%) | 22.2 | 51.9 | 38.92 | 46.2 | 33.92 | 46.2 |

O (%) | 16.12 | 29 | 31.23 | 40.98 | 31.23 | 40.98 | |

H (%) | 4.17 | 6.73 | 4.17 | 7.62 | 4.62 | 6.23 | |

N (%) | 1.86 | 10.92 | 0.63 | 10.92 | 3.42 | 4.23 | |

Proximate composition | Fc (%) | 0.02 | 9.87 | 0.82 | 25.86 | 1.21 | 29.26 |

V (%) | 45.98 | 83.62 | 71.52 | 87.23 | 29.26 | 39.5 | |

A (%) | 15.21 | 48.23 | 0.87 | 21.74 | 5.47 | 17.63 | |

Operational conditions | T (°C) | 150 | 320 | 150 | 320 | 150 | 320 |

T (min) | 9 | 220 | 8 | 220 | 5 | 220 | |

WC (%) | 75.23 | 95.24 | 74.86 | 95.87 | 94.97 | 74.56 |

**Table 2.**Machine learning studies are compared to past works focused on waste to resource management.

References | Process of Waste Conversion | Feedstock Types | Size of the Data Set | Machine Learning Model | Task Type | R^{2} Testing |
---|---|---|---|---|---|---|

Li et al. (2019) [22] | HTC | Organic wastes | 248 | Random Forest | Multi | 0.8–0.95 |

Ismail et al. (2019) [24] | HTC | Poultry litter | 21 | NN | Multi | >0.90 |

Jiang et al. (2019) [36] | HTC + pyrolysis | Straw | 30 | Linear Regression | Single | 0.098–0.99 |

SVR | Single | 0.98–0.99 | ||||

Li et al. (2020) [27] | HTC | Organic wastes | 649 | RF | Single | >0.90 |

475 | RF | Single | >0.90 | |||

Cheng et al. (2020) [37] | Hydrothermal treatment | Microalgae, crops/forest residues, and organic wastes | 800 | Multiple linear regression | Multi | 0.16–0.60 |

- | Regression tree | Multi | 0.29–0.75 | |||

- | RF | Multi | 0.70–0.90 | |||

Li, J., Zhu et al. (2020) [11] | HTC | Food waste, sludge, and manure | 248 | RF | Multi | 0.55–0.91 |

SVR | Multi | 0.88–0.96 | ||||

NN | Multi | 0.88–0.95 | ||||

This Work | HTC | Sewage sludge, food waste, and cattle manure | 281 | Ensemble SVM | Multi | 0.89–0.97 |

Properties | Maximum Fuel Properties of Pareto Solution | Maximum CCS Stability of Pareto Solution | ||||
---|---|---|---|---|---|---|

Sewage Sludge | Cattle Manure | Food Waste | Sewage Sludge | Cattle Manure | Food Waste | |

C (%) | 50.98 | 48.40 | 63.87 | 50.98 | 48.40 | 63.87 |

O (%) | 17.54 | 32.01 | 11.01 | 23.78 | 38.76 | 15.21 |

H (%) | 4.31 | 5.09 | 3.25 | 4.31 | 5.09 | 3.25 |

N (%) | 9.04 | 4.11 | 12.56 | 4.87 | 3.49 | 9.06 |

Fc (%) | 11.02 | 13.23 | 15.26 | 8.04 | 13.11 | 12.01 |

V (%) | 70.86 | 74.52 | 71.91 | 75.36 | 82.63 | 76.71 |

A (%) | 20.32 | 13.65 | 11.87 | 14.32 | 6.31 | 13.44 |

T (°C) | 223.00 | 205.42 | 285.65 | 297.93 | 328.98 | 327.78 |

t (min) | 6.00 | 6.00 | 6.08 | 29.56 | 57.06 | 14.14 |

WC (%) | 76.02 | 76.05 | 76.02 | 88.04 | 74.91 | 95.78 |

HHV (MJ/kg) | 29.37 | 21.66 | 36.43 | 27.11 | 25.44 | 33.21 |

YIELD (%) | 70.31 | 80.06 | 70.65 | 65.07 | 46.57 | 66.18 |

ER (%) | 104.87 | 95.32 | 113.27 | 96.45 | 71.65 | 107.33 |

CR (%) | 92.54 | 92.41 | 106.21 | 91.53 | 65.65 | 103.26 |

C_char (%) | 66.98 | 53.27 | 82.74 | 67.61 | 66.46 | 84.71 |

H/C | 0.82 | 2.02 | 0.21 | 0.62 | 0.95 | 0.12 |

N/C | 0.23 | 0.08 | 0.23 | 0.08 | 0.1 | 0.07 |

O/C | 0.01 | 0.57 | 0.01 | 0.03 | 0.18 | 0.01 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Velusamy, P.; Srinivasan, J.; Subramanian, N.; Mahendran, R.K.; Saleem, M.Q.; Ahmad, M.; Shafiq, M.; Choi, J.-G. Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste. *Sustainability* **2023**, *15*, 6088.
https://doi.org/10.3390/su15076088

**AMA Style**

Velusamy P, Srinivasan J, Subramanian N, Mahendran RK, Saleem MQ, Ahmad M, Shafiq M, Choi J-G. Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste. *Sustainability*. 2023; 15(7):6088.
https://doi.org/10.3390/su15076088

**Chicago/Turabian Style**

Velusamy, Parthasarathy, Jagadeesan Srinivasan, Nithyaselvakumari Subramanian, Rakesh Kumar Mahendran, Muhammad Qaiser Saleem, Maqbool Ahmad, Muhammad Shafiq, and Jin-Ghoo Choi. 2023. "Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste" *Sustainability* 15, no. 7: 6088.
https://doi.org/10.3390/su15076088