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

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## Abstract

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^{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

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**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 |

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## 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