# Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine

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## Abstract

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_{x}). Estimated values and results of experiments were compared. The error analysis showed that the built model has quite accurately predicted the experimental results. This has been described by the value of Coefficient of determination (R

^{2}), which varies between 0.8493 and 0.9863 with the value of normalized mean square error (NMSE) between 0.0071 and 0.1182. The potency of the Nash-Sutcliffe coefficient of efficiency (NSCE) ranges from 0.821 to 0.8898 for BTE, HC, NO

_{x}and Smoke. This research has effectively emulated the on-board efficiency, emission, and combustion features of a dual-fuel biogas diesel engine taking the Swish activation mechanism in artificial neural network (ANN) model.

## 1. Introduction

^{2}value ranges from 0.99 and 1. Kakatia et al. [5] used log-sigmoid to forecast the output for Soot, HC, CO2, NO

_{x}, CO and BSFCeq, taking inputs as oxygen, methanol flow rate, diesel flow rate, and air flow rate etc. Hariharana et al. [6] carried out experiments to assess the effects of using hydrogen (H2) and Lemon Grass Oil (LGO) as a selective diesel replacement fuel, in a Compression Ignition (CI) engine with single-cylinder. The ANN model has been developed using a regular backpropagation algorithm to predict the association between engine performance responses and input factors (i.e., load, LGO and hydrogen). To forecast brake specific fuel consumption (BSFC), overall in-cylinder pressure and exhaust emissions, Agbulut et al. [7] used ANN. For BSFC, NO

_{x}, CO, HC, and CPmax the R

^{2}value obtained was 0.9995, 0.9999, 0.9902, 0.9990, and 0.9979 respectively.

^{2}values of 0.999978–0.999998. Tests conducted by Shukri et al. [11], indicated that the blend of diesel fuel with palm oil and methyl ester have improved the engine efficiency. For the in-cylinder pressure, heat release, thermal efficiency, and volume, the R

^{2}value of 0.996, 0.999, 0.989 and 0.998 was obtained respectively.

^{2}values of 0.99 were obtained; RMS values were lower than 0.02; and for test results, mean error percentage (MEP) values were lower than 2.7 percent. The sigmoid function was found to be the most commonly used activation function in models of the Artificial Neural Network in most of the studies so highlighted. The output varies from 0 to 1 for the sigmoid function, and from 0 to 0.25 for the derivatives of the sigmoid function. The Sigmoid is usually susceptible to the issue of vanishing gradient and method outcome is not zero-centered. In addition, the exponent and power operations make it costly to compute.

#### Motivating Factor for This Research Work

_{x}, Smoke. The researchers used the swish activation function to build a three layered ANN model.

## 2. Materials and Methods

^{3}capacity. Deenbandhu-based biogas plant has been traditionally used in provincial territories as cooking fuel. In a vault, the composite gas was stored and piped into the engine. To examine the physiochemical properties of fuels, the set standard of the American Society for Testing and Materials (ASTM) was used. Table 1 displays some associated fuel characteristics.

#### Experimental Setup

_{x}, and Smoke were measured using a Di-gas analyser (AVL 4000). The % volume has been used to note CO and Smoke, whereas, gm/kW.hr for both HC and NO

_{x}. In order to measure smoke exhalation, a diesel smoke metre (AVL 437) has been used; particularly the smoke opacity. In compliance with ASTM-D6522, the exhalations of gas are strictly regulated.

## 3. Application of Artificial Neural Network (ANN)

#### 3.1. Back Propagation and ANN for Current Study

_{x}and Smoke. Figure 2 demonstrates the ANN model used here in this study work and Table 4 lists the parameter values used in ANN.

#### 3.2. Swish Activation Function and Its Importance

#### 3.3. Count of Neurons for the Hidden Layer in ANN

#### 3.4. Selection among Sigmoid and Swish

_{x}, and Smoke, the value of RMSE was determined taking hidden neuron counts as 3, 5, and 7. ANN model employs sigmoid and swish activation function separately. Table 5 reveals that the RMSE values obtained for swish activation are lower relative to the commonly used sigmoid function, which proves swish, a better choice than the sigmoid. Also, when compared with rectified linear unit (ReLU) activation function, Swish is as effective as ReLU in computation, but demonstrates greater efficiency than ReLU. Swish values vary from infinity to infinity in the negative. The function curve is smooth and at all points the function is distinguishable, which is one of the reasons for outperforming swish from ReLU.

## 4. Model Evaluation

#### 4.1. Metrics for Evaluation

^{2}measure Equation (5), [25], which was found to be constrained by its intrinsic sensitivity to the expected and observed values of means and variances.

#### 4.2. Solver Architecture

^{2}, RMSE, NMSE, NRMSE, MSRE, NSCE, Theil, and KL values computed at 3, 5, and 7 hidden layer neurons using ANN with SWISH activation function.

_{x}and Smoke.

#### 4.3. Uncertainty Analysis for the Proposed Model

_{x}, and Smoke; considering load, bio-gas flow rate, n-butanol as data, which was obtained from the experimental results. In this study, the model’s predictability toward engine responsiveness showed good alignment with statistics of correlation. However, the complete uncertainty involved in measurement model derives from two distinct factors. One is the uncertainty of Theil that was considered in the development of the ANN model and the second is related to experimental tools. Total uncertainty estimation is seen in Table 7 using the Equation (12).

## 5. Results and Discussion

_{x}experimental outcomes. It exhibits remarkably low 0.0805 Theil uncertainty along with 0.0064 MSRE. Other statistical tests such as NMSE, NRMSE also displayed very low significance, i.e., 0.0071 and 0.0897. NSCE was observed as high as 88.98 percent in special error metrics, while KL-N was reported to be 0.080 which fulfilled the stronger compatibility of expected values with experimental findings. Figure 11 showed the similarity of forecasted smoke by the model presented with observed smoke through experiments. The values for Smoke MSRE, NMSE and NRMSE were 0.0625, 0.1182 and 0.1872, respectively. With Theil’s uncertainty as low as 0.1152, the model designed scored NSCE efficiency as high as 82.09 percent. In particular, a very low value of 0.0079 showed the KL-N divergence effectiveness, suggesting its good predictive accuracy.

## 6. Conclusions

^{2}as 0.8493 and maximum as 0.9863. Also the values for NMSE spans between 0.0071 to 0.1182. NSCE performance ranged from 0.821 to 0.8898 for BTE, HC, NO

_{x}and Smoke. The NSCE performance was found to range from 0.821 to 0.8898 for BTE, HC, NO

_{x}and Smoke. Therefore, it can be concluded that the on-board performance and exhaust characteristics of a dual-fuel biogas-diesel engine can be effectively simulated by the proven ANN model.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**Depiction for comparing NMSE, NRMSE and MSRE of the model taking (

**a**) 3 neurons; (

**b**) 5 neurons; and (

**c**) 7 neurons.

**Figure 5.**Illustration of R

^{2}and RMSE of the model for (

**a**) 3 neurons; (

**b**) 5 neurons; and (

**c**) 7 neurons.

**Figure 6.**Representation of values obtained with respect to Theil U2, NSE, and KL-divergence of the model for

**(a**) 3 neurons, (

**b**) 5 neurons, and (

**c**) 7 neurons.

**Figure 7.**(

**a**) Depiction of predicted and experimentally obtained BTE values taken for 20 test cases; (

**b**) Regression coefficient for the values predicted using ANN Vs experimental.

**Figure 8.**(

**a**) Depiction of predicted and experimentally obtained CO values taken for 20 test cases; (

**b**) Regression coefficient for the values predicted using ANN Vs experimental.

**Figure 9.**(

**a**) Depiction of predicted and experimentally obtained HC values taken for 20 test cases; (

**b**) Regression coefficient for the values predicted using ANN Vs experimental.

**Figure 10.**(

**a**) Depiction of predicted and experimentally obtained NO

_{x}values taken for 20 test cases; (

**b**) Regression coefficient for the values predicted using ANN Vs experimental.

**Figure 11.**(

**a**) Depiction of predicted and experimentally obtained Smoke values taken for 20 test cases; (

**b**) Regression coefficient for the values predicted using ANN Vs experimental.

**Table 1.**Various traits of the test fuel along with its ASTM based test scheme (# Not provided by the supplier).

Features | Diesel | n-Butanol | Test Scheme (ASTM) | Biogas | Test Scheme (ASTM) |
---|---|---|---|---|---|

Density (kg/m^{3})@15 °C | 840 | 810 | D4052 | 0.92 | D3588 |

Viscosity (mm^{2}/s)@40 °C | 2.72 | 3.64 | D445 | - | |

Heating Value (MJ/kg) | 42.6 | 33.2 | D4809 | 26.23 | D1945 |

Flash Point (°C) | 78 | 35 | D93 | - | |

Fire Point (°C) | 83 | 42 | D93 | ||

Cloud Point (°C) | −8 | # | DL500 | ||

Pour Point (°C) | −6 | −45 | D97 | ||

Cetane Number (CN) | 50 | 22 | D613 |

Chemical Formula | C_{4}H_{10}O |
---|---|

Boiling point | 117 (°C) |

Temperature for Auto ignition | 343 (°C) |

Octane number | 96 |

Oxygen | 21.62 (% by weight) |

Latent heat of vaporization at 25 °C | 626 (kJ/kg) |

Make | Kirloskar |
---|---|

Model Specifications | TV 1 |

System used for Cooling | Water Cooled |

Cylinder Count | 01 |

Rated Brake Power | 5.2 kW @ 1500 rpm |

Displacement volume | 661 (cc) |

Rated Speed | 1500 (rpm) |

Standard Fuel Injection Timing | 23° before TDC |

Bore × Stroke | 87.5 × 110 (mm) |

Compression Ratio | 17.5:1 |

Structure of the Network Used Here | 3 inputs, 01 hidden layer, and 5 outputs |

Percentage of Data used for training and testing | Training: 130 rows for training Testing: 20 rows for testing |

Type of the Network | Feed Forward Back Propagation |

Function used for Training | Backpropagation |

Optimization Function | Adam |

Transfer/Activation Function | Swish |

Criteria used to Stop | On-set of enhancement in validation error will results into breaking of training network |

**Table 5.**RMSE value for BTE, CO, HC, NO

_{x}, and Smoke computed using swish and sigmoid activation function taking number of neurons as 3, 5 and 7 at hidden layer of the ANN model.

O/P | Transfer Function (At Layer 1-2-3) | No. of Neurons | RMSE |
---|---|---|---|

BTE | swish | 3 | 1.741 |

5 | 3.927 | ||

7 | 3.288 | ||

sigmoid | 3 | 15.713 | |

5 | 15.693 | ||

7 | 15.691 | ||

CO | swish | 3 | 0.078 |

5 | 0.059 | ||

7 | 0.056 | ||

sigmoid | 3 | 0.08 | |

5 | 0.067 | ||

7 | 0.078 | ||

HC | swish | 3 | 0.286 |

5 | 0.304 | ||

7 | 0.307 | ||

sigmoid | 3 | 0.69 | |

5 | 0.61 | ||

7 | 0.57 | ||

NO_{x} | swish | 3 | 1.491 |

5 | 1.435 | ||

7 | 2.207 | ||

sigmoid | 3 | 16.94 | |

5 | 16.863 | ||

7 | 16.86 | ||

Smoke | swish | 3 | 2.448 |

5 | 3.154 | ||

7 | 3.170 | ||

sigmoid | 3 | 20.327 | |

5 | 20.326 | ||

7 | 20.31 |

**Table 6.**Computation of R

^{2}, RMSE, NMSE, NRMSE, MSRE, NSCE, Theil, KL using 3, 5, and 7 neurons at hidden layer with swish activation function while computing BTE, CO, HC, NO

_{x}, and Smoke.

O/P | TF (At Layer 1-2-3) | No. of Neurons | R^{2} | RMSE | NMSE | NRMSE | MSRE | NSCE | Theil | KL |
---|---|---|---|---|---|---|---|---|---|---|

BTE | swish + swish + swish | 3 | 0.9705 | 1.741 | 0.0119 | 0.1235 | 0.0193 | 0.8211 | 0.1047 | 0.0068 |

5 | 0.915 | 3.927 | 0.0716 | 0.2786 | 0.0405 | 0.0898 | 0.2361 | 0.008 | ||

7 | 0.943 | 3.288 | 0.0489 | 0.2332 | 0.0282 | 0.3618 | 0.1977 | 0.007 | ||

CO | 3 | 0.6576 | 0.078 | 0.2652 | 0.2609 | 0.1182 | 0.2497 | 0.3802 | 0.025 | |

5 | 0.8475 | 0.059 | 0.1332 | 0.199 | 0.0673 | 0.5635 | 0.29 | 0.018 | ||

7 | 0.9332 | 0.056 | 0.1182 | 0.1872 | 0.0625 | 0.6139 | 0.2727 | 0.013 | ||

HC | 3 | 0.9863 | 0.286 | 0.0938 | 0.1359 | 0.0673 | 0.8450 | 0.2294 | 0.016 | |

5 | 0.9038 | 0.304 | 0.1069 | 0.1441 | 0.0686 | 0.8256 | 0.2434 | 0.019 | ||

7 | 0.9006 | 0.307 | 0.1101 | 0.1457 | 0.0629 | 0.8218 | 0.246 | 0.016 | ||

NO_{x} | 3 | 0.9923 | 1.491 | 0.0077 | 0.0932 | 0.0067 | 0.8810 | 0.0837 | 0.027 | |

5 | 0.9288 | 1.435 | 0.0071 | 0.0897 | 0.0064 | 0.8898 | 0.0805 | 0.028 | ||

7 | 0.9813 | 2.207 | 0.0178 | 0.138 | 0.0129 | 0.7393 | 0.1239 | 0.04 | ||

Smoke | 3 | 0.8493 | 2.448 | 0.0147 | 0.1224 | 0.0125 | 0.8209 | 0.1152 | 0.0079 | |

5 | 0.9817 | 3.154 | 0.0216 | 0.1577 | 0.028 | 0.7027 | 0.1484 | 0.0057 | ||

7 | 0.9798 | 3.170 | 0.0299 | 0.1585 | 0.0211 | 0.6997 | 0.1491 | 0.0086 |

**Table 7.**Total uncertainty values obtained taking uncertainty values of the measuring instrument and the proposed ANN model.

Parameters | Values for the Uncertainties | Computation | Cumulative Uncertainties U _{T} (%) |
---|---|---|---|

BTE | 2.5, 0.11 | $\sqrt{{\left(2.5\right)}^{2}+{\left(0.11\right)}^{2}}$ | 2.5024 |

CO | 0.2, 0.27 | $\sqrt{{\left(0.2\right)}^{2}+{\left(0.27\right)}^{2}}$ | 0.3360 |

HC | 0.1, 0.23 | $\sqrt{{\left(0.1\right)}^{2}+{\left(0.23\right)}^{2}}$ | 0.2507 |

NO_{x} | 0.2, 0.08 | $\sqrt{{\left(0.2\right)}^{2}+{\left(0.08\right)}^{2}}$ | 0.2154 |

Smoke | 1, 0.11 | $\sqrt{{\left(1\right)}^{2}+{\left(0.11\right)}^{2}}$ | 1.0060 |

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**MDPI and ACS Style**

Arora, V.; Mahla, S.K.; Leekha, R.S.; Dhir, A.; Lee, K.; Ko, H. Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine. *Electronics* **2021**, *10*, 584.
https://doi.org/10.3390/electronics10050584

**AMA Style**

Arora V, Mahla SK, Leekha RS, Dhir A, Lee K, Ko H. Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine. *Electronics*. 2021; 10(5):584.
https://doi.org/10.3390/electronics10050584

**Chicago/Turabian Style**

Arora, Vinay, Sunil Kumar Mahla, Rohan Singh Leekha, Amit Dhir, Kyungroul Lee, and Hoon Ko. 2021. "Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine" *Electronics* 10, no. 5: 584.
https://doi.org/10.3390/electronics10050584