1. Elnaz Siami-Irdemoosaa,Saeid R. Dindarloo
Prediction of fuel consumption of mining dump trucks: A neural networks approach
Elnaz Siami-Irdemoosaa,Saeid R. Dindarloo
Applied Energy
Volume 151, 1 August 2015, Pages 77–84
Reporter：HUANG, REN-JUN
XU, XIANG-SHENG

2. OUTLINE Introduction Methods and materials Discussion and results
Conclusions

3. 1. Introduction
Dump trucks are the main haulage equipment in surface mining which account for a considerable amount of both capital and operational costs.
Fuel consumption accounts for about 30% of total energy use in surface mines.
採礦車採礦需要相當大量的投資和成本。
採礦車採礦時燃油消耗約佔能源使用總量的30％。

4. 1. Introduction
A fleet of large dump trucks is the main source of greenhouse gas (GHG) generation.
Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG generation.
大型採礦車也是溫室氣體（GHG）產生的主要來源。
建立模型預測評估能源消耗是相當重要的 會影響到能源成本及溫室氣體產生

5. 1. Introduction
Tries to predict heavy mining dump trucks’ fuel consumption per cycle based on cyclic haulage activities.
Fuel consumption per cycle of operation was predicted using artificial neural networks technique
本文基於每個週期的循環運輸來預測採礦車的油耗。
作者利用人工神經網絡技術預測每次操作的循環油耗

6. 2. Methods and materials Explanatory variables were: pay load (PL)
loading time (LT)
idled while loaded (LS)
loaded travel time (LTR)
empty travel time (ETR)
idled while empty (ES)
the output variable was the amount of fuel consumed in one cycle (F).
Table. 1. Descriptive statistics of the variables.
F-燃料燃燒週期
PL-有效載負 LT-加載時間 LS-閒置時加載
LTR-裝載工作時間 ETR-無裝載工作時間 ES-閒置時未裝載

7. 2. Methods and materials
本研究使用反向傳播網絡（BPN）
在輸入層的神經元的數目是6，輸出層僅由一個神經元。隱藏層的數目和隱藏神經元的數量在驗證階段
Fig. 1. Primary structure of the proposed network.

8. 2. Methods and materials
Table .2 Proposed value for learning rate (η) and momentum (μ) in different studies.
在模擬的第一屆階段，作者參考表2設定學習率及動量
驗證涉及使用訓練和測試的子集，並且同時評估網絡的性能通過分析的均方誤差（MSE）所提出的神經網絡模型的培訓

9. 2. Methods and materials
每種組合被訓練以同一套初始權重和相同的訓練數據集。
圖2 為MSE作為學習的進行各種組合。
學習率為(a)0.01 (b)0.1 (c)0.5 (d)0.9 分別對變量M=0, 0.1, 0.5, 0.9
find(a)0.9 (b)0.5(c)0.1 [因為0.9沒有達到收斂] (d)0
Fig. 2. Learning performance: (a) learning rate = 0 and momentum varies,
(b) learning rate = 0.1 and momentum varies, (c) learning rate = 0.5 and momentum varies.
(d) learning rate = 0.9 and momentum varies.

10. 2. Methods and materials
從四個情況下，最好的學習曲線繪製在一起，以確定最佳的整體學習曲線。
最佳曲線由組合10（學習的0.5和0.1速率和動量的組合
Fig. 3. Best learning curves from different training trials.

11. 2. Methods and materials
Table 4. Models training results for different combination of learning rate (η) and momentum (μ).
表4是MSE值的16種組合。第10組合被確定為學習的最佳組合速率（η動量）和（μ）。
12組合的學習曲線從未收斂到最佳。

12. 2. Methods and materials
在模擬的第二階段，10個獨立的模式具有不同的初始隨機權重
Fig. 4-1. Learning performance for 10 different initial weight groups (WG1–WG4).

13. 2. Methods and materials
Fig. 4-2. Learning performance for 10 different initial weight groups (WG4–WG8).

14. 2. Methods and materials
Fig. 4-3. Learning performance for 10 different initial weight groups (WG9–WG10).

15. 2. Methods and materials
Table 5. Models training results for different initial weight groups.

16. 2. Methods and materials
Table 6. Proposed formula for determination of the size of hidden layer in different studies.
接下來是找出最好的隱藏層大小。具有一定數目的節點，作者參考表6。每次，隱藏節點的數目增加一。再次，在交叉驗證來確定隱藏層的適當大小。

17. 3. Discussion and results
Table 7 Structure of different neural model with variable number of hidden neuron.
該模型進行了檢查對測試數據（訓練時正在進行中）。此外，該模型應該為它的泛化能力進行驗證。在此步驟中，它的目的是確認所提出的模型的能力準確地響應了未在網絡發展中使用的那些數據。均方根誤差（RM​​SE）和MAPE度量被用來驗證該模型的結果。
兩個隱藏層中的沿第一9隱藏神經元和3隱藏神經元在第二層中產生了最小MSE值。應當指出的是，學習速率，動量和初始權重是在以前的階段確定並固定在這個階段。

18. 3. Discussion and results
Fig. 5. Proposed ANN model for predicting fuel consumption.

19. 3. Discussion and results
ANN表現出優異的業績，為501個週期10％MAPE值。
Fig. 6. Actual vs. ANN predicted fuel consumption.

20. 4. Conclusions
A feed-forward neural network with back-propagation algorithm was used to predict the fuel consumption per cycle.
Application of preventive remedies such as optimized dispatching strategies in combination with corrective remedies such as application of new technologies result in reducing mining trucks idle times, and hence, reduces both the energy consumption and exhaust emissions.
前饋神經網絡BP算法來預測每個循環的耗油量。
預防補救措施，如在整改補救的組合優化調度策略，如應用新技術的應用導致減少礦用卡車的空閒時間，因此，降低了能源消耗和廢氣排放。