# Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System

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## Abstract

**:**

## 1. Introduction

^{137}Cs radioisotope, and a tube [5]. In subsequent research, they looked into the viability of using GMDH neural networks to detect distinct flow regimes and forecast volume fractions [6]. This research accurately calculated the volume percentage; however, it paid little attention to the quantity of scale in the pipe. Ba-133 and Cs-137 are used to measure the thickness of the scale in the oil pipe [7]. An RBF neural network was trained using data obtained from a simulation of the two-phase flow in various regimes, including information on detector properties for both transmission and scattering detectors. The RMSE for their predicted scale thickness was less than 0.22; therefore, their hard work paid off. 133 Ba and 241 Am were used to analyze the scale layer in an oil pipe in recent research. It was proposed that the photopeaks of 133 Ba and 241 Am from two detectors be used as inputs to the RBF neural network after modelling the three-phase flow in annular regimes. Eventually, they achieved an RMSE of less than 0.09 when estimating the thickness of scales [8]. Always-on, radioisotope-based energy sources have drawbacks, including the necessity for personnel to wear protective clothing and transportation constraints. Hence, X-ray tube research into measuring multiphase flow properties has gained traction in recent years [9,10,11,12]. X-rays and a NaI detector were used to determine the flow regime and volume fraction of two-phase flows in [9]. Two multilayer perceptron networks were trained using the detector’s signal timing properties. Models of a homogeneous flow, an annular flow, and a stratified flow were used to examine three-phase flows at varying volume fractions in [10]. Moreover, three RBF networks were trained using the input signals’ rather exact frequency characteristics. In [11], the MCNP method was used to model the X-ray tube’s core as it passed through a mixture of four petroleum products blended in pairs of varying concentrations. Three multilayer perceptron neural networks were fed the recorded signals, and their output was a prediction of the volume distribution of the three products. The fourth product’s volume ratio was easily determined after the volume ratios of the previous three were known. The presented method foresaw the objects’ types and quantities, but it lacked feature extraction techniques and therefore could not be very precise. Balubaid et al. [12] looked at the use of wavelet transformations as a method of feature extraction to expand previous studies [11]. The computing load was optimised, and accuracy was enhanced as a result of this effort. Large-scale combinatorial issues and nonlinear problems are beyond the capabilities of traditional optimisation techniques. Therefore, optimisation techniques based on metaheuristics have been presented. The nine categories used to assess general-purpose metaheuristic methodologies are as follows: biological, physical, social, musical, chemical, athletic, mathematical, swarm, and hybrid. Recent studies on plants have shown that they are capable of complex behaviours indicative of intelligence. Therefore, it is postulated that plants have some kind of neurological system. Algorithms and software programmes related to plant intelligence were compiled and analysed in [13]. These algorithms include the Flower Pollination Algorithm, Invasive Weed optimisation, Paddy Field Algorithm, Root Mass optimisation Algorithm, Artificial Plant optimisation Algorithm, Sapling Growing Up Algorithm, Photosynthetic Algorithm, Plant Growth Optimisation, Root Growth, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree optimisation. Due to the attitude of always seeking the best and the lack of the most efficient algorithm for all sorts of issues, new techniques or new versions of current methods are offered to test their ability to handle very complicated optimisation challenges. Recent work has presented two methods that seem to be light-based intelligent optimisation algorithms; these are ray optimisation and optics-inspired optimisation [14]. The principles of light refraction and reflection serve as inspiration for modern intelligent search and optimisation algorithms.

- Examining the time and frequency characteristics simultaneously in order to determine the thickness of the scale layer.
- Using feature selection techniques based on the ACO algorithm to determine effective features.
- Significant increase in the accuracy of the detection system by using appropriate specifications.
- Reducing the amount of computation applied to the neural network by selecting the appropriate features in a manual process.

## 2. Simulation Setup

^{3}, and the density of oil is 0.826 g/cm

^{3}. MCNP was utilised to implement the framework in this research. It is important to note that previous studies [1] have confirmed the accuracy of the simulations used in this analysis. Several experimental setups were built in this investigation and compared to information gleaned from the MCNP program. The MCNP algorithm’s Tally output was translated to units per source particle to enable a direct comparison between experimental and simulated results. A 2.2% relative error was found between the simulation and the experimental arrangement. Using the 36 possible volume percentages for each of the 7 values of the scale thickness, a total of 252 simulations could be generated. The whole of the required construction is shown in Figure 2. Figure 3 and Figure 4 provide a visual representation of the signals that were captured by the first and second detectors at different scale thicknesses. In order to explain the attenuation of a narrow beam of gamma rays, LamberteBeer’s law states:

_{0}) are denoted here. The absorber density, represented by $\rho $, and the mass attenuation coefficient, denoted by µ. x represents the total distance a beam travels through an absorber. This formula predicts that the detector will record a range of intensities as a result of photons hitting with different materials. When a three-phase flow travels through the pipeline, this change in measured intensity can be used to determine the scale thickness. In this research, all the simulations have been carried out under the equal conditions, with the difference of the volumetric percentages and of the thickness of the scale layer inside the pipe. It is necessary to mention that using Pulse Height Tally F8 in the MCNPX code, we were able to determine how many particles were detected by the transmission detector for every one that originated from the source. When the count was as precise as needed, the STOP card was used to stop the process. The STOP card was used to limit relative errors in all simulations to less than 0.005, therefore all Monte-Carlo findings are accurate to within this range.

## 3. Feature Extraction

#### 3.1. Time-Domain Feature Extraction

^{exp th}root (ASM):

_{n}is the main time-domain signal.

#### 3.2. Frequency-Domain Feature Extraction

## 4. Ant Colony Optimization

#### 4.1. Algorithmic Design

_{1}and R

_{2}, respectively. Hence, the initial probability of path selection (between E1 and E2) for each ant can be written as follows [21]:

_{1}> R

_{2}, then the odds of picking E1 are greater, and vice versa. Now, on the way back by the shortest path, say Ei, the pheromone value is updated for the associated route. Pheromone evaporation rates and route lengths are taken into account for this revision. This means that the upgrade can be implemented in stages.

#### 4.2. In Accordance to Path Length

#### 4.3. ACO-Based Feature Selection

## 5. MLP Neural Network

## 6. Results

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Signals recorded by the first detector (

**a**) 0 cm scale, (

**b**) 0.5 cm scale, (

**c**) 1 cm scale, (

**d**) 1.5 cm scale, (

**e**) 2 cm scale, (

**f**) 2.5 cm scale, and (

**g**) 3 cm scale.

**Figure 4.**Signals recorded by the second detector (

**a**) 0 cm scale, (

**b**) 0.5 cm scale, (

**c**) 1 cm scale, (

**d**) 1.5 cm scale, (

**e**) 2 cm scale, (

**f**) 2.5 cm scale, and (

**g**) 3 cm scale.

Parameter | Value |
---|---|

Number of selected features | 1–30 |

Cost function of the best mode | 0.86 |

Maximum Number of Iterations | 20 |

Number of Ants (Population Size) | 15 |

Initial Pheromone | 1 |

Pheromone Exponential Weight | 1 |

Heuristic Exponential Weight | 1 |

Evaporation Rate | 0.05 |

Ref. | Extracted Features | Feature Selection Method | Type of Neural Network | Maximum MSE | Maximum RMSE |
---|---|---|---|---|---|

[5] | Time features | Lack of feature selection | GMDH | 1.24 | 1.11 |

[6] | Time features | Lack of feature selection | MLP | 0.21 | 0.46 |

[54] | No feature extraction | Lack of feature selection | MLP | 2.56 | 1.6 |

[55] | Lack of feature extraction | Lack of feature selection | GMDH | 7.34 | 2.71 |

[56] | Frequency features | Lack of feature selection | MLP | 0.67 | 0.82 |

[57] | Wavelet features | Lack of feature selection | GMDH | 0.19 | 0.44 |

[58] | Full energy peak (transmission count), photon counts of Compton edge in the transmission detector and total count in the scattering detector | Lack of feature selection | MLP | 1.08 | 1.04 |

[59] | Frequency and wavelet features | PSO-based feature selection | MLP | 0.13 | 0.36 |

[60] | Time, wavelet, and frequency features | PSO-based feature selection | GMDH | 0.09 | 0.30 |

[current study] | Time and frequency features | ACO-based feature selection | MLP | 0.0002 | 0.017 |

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**MDPI and ACS Style**

Mayet, A.M.; Ijyas, V.P.T.; Bhutto, J.K.; Guerrero, J.W.G.; Shukla, N.K.; Eftekhari-Zadeh, E.; Alhashim, H.H. Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System. *Processes* **2023**, *11*, 1621.
https://doi.org/10.3390/pr11061621

**AMA Style**

Mayet AM, Ijyas VPT, Bhutto JK, Guerrero JWG, Shukla NK, Eftekhari-Zadeh E, Alhashim HH. Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System. *Processes*. 2023; 11(6):1621.
https://doi.org/10.3390/pr11061621

**Chicago/Turabian Style**

Mayet, Abdulilah Mohammad, V. P. Thafasal Ijyas, Javed Khan Bhutto, John William Grimaldo Guerrero, Neeraj Kumar Shukla, Ehsan Eftekhari-Zadeh, and Hala H. Alhashim. 2023. "Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System" *Processes* 11, no. 6: 1621.
https://doi.org/10.3390/pr11061621