# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 4 Simulation and Analysis
### Horizontal Bending Angle Optimization Effect Analysis  
In the actual working condition of the mine, the S-bending area of the scraper conveyor, also called transition section, is formed by the hydraulic support pushing the middle groove to the next cutting plane. For the unmined cutting face, shearer cutting coal rock usually starts chamfering in the S-bending area. Therefore, the horizontal bending angle of the S-bending area has an important influence on the production of working face and the chamfering feed of the shearer.  
The fitness function was established in combination with the actual working conditions under the mine. The higher was the evaluation score, the higher was the production efficiency of the working face. The parameter information of the working face was inputted into the fitness function. Then, the four algorithms of ABAM, BCBA, BAO and MBA were used to iteratively search for the highest score (\(S_{\text{score-max}}\)) and the corresponding optimal \(\alpha^{\prime}\). Compare the paths searched by 4 different algorithms 3 times, and the comparison results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object** & **Technical Parameters** & **Value** \\ \hline Working face & Length (m) & 270 \\ \hline Shearer & Height (m) & 2 \\ (MG300/730-WD2) & Cut depth (m) & 0.63 \\  & Haulage speed (m/min) & 4.5 \\ \hline  & Chain speed (m/s) & 1.31 \\ Scraper conveyor & Motor & YBSD-400/200-4/8 GX3 \\ (SGZ800/800) & Transportation volume (t/h) & 1500 \\  & Middle slot (mm) & \(1503\times 800\times 308\) \\  & Scraper spacing (mm) & 1008 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coal mining face and equipment parameters.  
Figure 5: _Cont._As shown in Figure 5, the four optimization algorithms were iterated 300 times, and the evaluation score of the search was small, about 92.4 points. In the process of searching, the MBA algorithm reached the optimal value by 40 iterations, while the ABAM and BCBA needed 70 iterations, and the BAO needed 90 iterations, as shown in Figure 5a. when \(\alpha^{\prime}\) reached the optimum value of 0.66\({}^{\circ}\), the MBA had iterated 40 times, while the BAO 100 times. The ABAM and BCBA algorithm search process oscillated very sharply, and began to stabilize in the 150 th generation, as shown in Figure 5b.  
To improve the production efficiency of the working face when the shearer bevel cutting, this study combined the running direction of the shearer and optimized the \(\alpha^{\prime}\) of the S-bending area by using the proposed method and three other algorithms. The optimization results are shown in Figure 6.  
Figure 5: Comparison of optimization results of four different optimization algorithms.  
Figure 6: Optimal horizontal bending angle and corresponding score distribution map.  
As shown in Figure 6, in the shearer level cutting stage, the horizontal bending angle \(\alpha^{\prime}\) of the S-bending area was [0.60\({}^{\circ}\), 0.75\({}^{\circ}\)], [0.57\({}^{\circ}\), 0.66\({}^{\circ}\)]. The optimal horizontal bending angle was not linear with the cutting time. Because the mine environment is complex and variable, the value of the \(\alpha^{\prime}\) was different when the shearer cuts forward and reverses. In the shearer cutting process, the working surface evaluation score increased from 50 points to 92.4 points.