# Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships

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## Abstract

**:**

^{2}of 0.97. Furthermore, in the fuel consumption optimization stage, the particle swarm optimization method can effectively reduce fuel consumption. This study helps PCTC companies control shipping costs and save energy. Insights for shipping businesses to meet environmental demands are provided as well.

## 1. Introduction

## 2. Literature Review

#### 2.1. Linear Regression-Based Black Box Model

#### 2.2. Literature Review of Machine Learning-Based Black Box Models

#### 2.3. Literature Review of Ship Fuel Consumption Optimization

_{2}emissions is scarce. Fifthly, existing ship fuel consumption prediction models are weak at analyzing the uncertainty of ship motion. Deep reinforcement learning (DRL) provides research ideas for solving the modeling of ship motion recognition in complex scenarios.

## 3. Research Methodology

#### 3.1. General Framework

#### 3.2. Modeling Methods

#### 3.2.1. XGBoost Algorithm Framework

#### 3.2.2. Particle Swarm Optimization Algorithm Framework

- (1)
- The size, starting location, and beginning velocity of each particle are all part of the initialization process for a swarm of particles.
- (2)
- Find each particle’s fitness value using the objective function, then set the local and global optimum values to start with. Regarding the fitness function’s design, we may generate problem-specific designs. The core idea is that the size of the fitness value can determine whether the particle’s position is optimal.
- (3)
- Determine the termination condition’s achievement. If the goal is reached, the search process ends with the returned results. If not, proceed with the procedures that follow.
- (4)
- Change the velocities and positions of the particles in accordance with the formula for changing velocities and positions.
- (5)
- Determine the fitness of each particle according to the goal.
- (6)
- Refresh the global and local best values for each particle.
- (7)
- Set the termination condition of the iteration based on the specific problem, typically reaching the specified maximum number of iterations or the current optimal position of the particle swarm in order to satisfy the search requirements.

#### 3.3. Ship Description

#### 3.4. Description of the Data Set

#### 3.5. Data Pre-Processing

## 4. Results

#### 4.1. Modeling

^{2}for each model after 100 training trials are displayed in Table 4. The results of the trained models on the test sets and the training sets are as follows (MSE and R

^{2}are evaluation indicators):

Models | Training Set | Test Set | Training Set R^{2} | Test Set R^{2} |
---|---|---|---|---|

Linear model | 3.962 × 10³ | 2.603 × 10³ | 0.738 | 0.780 |

Random Forest | 4.037 × 10⁴ | 1.341 × 10³ | 0.973 | 0.886 |

DT | 0 × 10⁰⁰ | 3.127 × 10³ | 1.000 | 0.735 |

SVM | 2.874 × 10³ | 1.797 × 10³ | 0.810 | 0.832 |

KNN | 1.952 × 10³ | 1.797 × 10³ | 0.871 | 0.848 |

Adaboost | 3.622 × 10³ | 2.830 × 10³ | 0.760 | 0.760 |

GBRT | 1.683 × 10³ | 1.244 × 10³ | 0.889 | 0.895 |

Bagging | 4.755 × 10⁴ | 1.454 × 10³ | 0.969 | 0.877 |

ExtraTree | 0 × 10⁰⁰ | 3.621 × 10³ | 1.000 | 0.693 |

LASSO | 1.511 × 10² | 1.202 × 10² | 0.000 | 0.018 |

MLP | 3.068 × 10³ | 1.873 × 10³ | 0.797 | 0.841 |

SGD | 8.377 × 10³ | 6.312 × 10³ | 0.446 | 0.466 |

XGBR | 2.406 × 10⁴ | 9.633 × 10⁴ | 0.984 | 0.968 |

BP NN | 2.861 × 10³ | 3.649 × 10³ | 0.837 | 0.744 |

RBF NN | 2.738 × 10⁴ | 3.128 × 10³ | 0.844 | 0.780 |

#### 4.2. Optimization

#### 4.2.1. Optimization Process

#### 4.2.2. ASNW Route Optimization

#### 4.2.3. Optimization of the ASAG Route

#### 4.2.4. Optimization of the ASEU Route

## 5. Conclusions and Discussion

^{2}value of 0.97) for estimating pure car/truck ship energy consumption and logistics costs in the shipping industry. Subsequently, we proposed a particle swarm optimization model in the second stage. By calculating the optimal sailing speed for each segment of the voyage, we can significantly cut down on the ship’s CO

_{2}emissions and fuel usage throughout the course of the whole trip. In addition, we analyzed the degree of influence of input characteristics on total fuel consumption.

^{2}for the XGBoost model is 0.97, which is the maximum value). Second, speed optimization can effectively increase the energy efficacy of ships and reduce bunker costs by a considerable amount. Third, the impact of route and days in port on ship petroleum consumption varies among the tested algorithms, as does the effect on predicted performance. In the end, we studied and modeled the vessel’s actual fuel usage, then optimized fuel consumption and overall cost throughout the whole range. We determined that the proposed XGBoost and PSO models have good accuracy and robustness.

## 6. Limitations and Future Research

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Full Name | Abbreviation |
---|---|

Pure car and truck shipping company | PCTC |

Black box model | BBM |

Machine learning | ML |

Particle swarm optimization | PSO |

International Maritime Organization | IMO |

Greenhouse Gas | GHG |

Multiple linear regression | MLR |

Artificial neural network | ANN |

Deep reinforcement learning | DRL |

Emission control areas | ECAs |

Mean absolute percentage error | MAPE |

Computational Fluid Dynamics | CFD |

Extreme gradient boosting | XGBoost |

Gradient Boosting Decision Tree | GBDT |

Pure car carrier | PCC |

Accent Equivalent Unit | AEU |

Marine Gas Oil | MGO |

Least absolute shrinkage and selection operator | LASSO |

Ridge regression | RIDGE |

Support vector regression | SVR |

K Nearest Neighbors | KNN |

Regressive Function | RF |

Decision Tree | DT |

Asia North West America | ASNW |

Asia Arabian Gulf | ASAG |

Asia Europe | ASEU |

380 Centistoke High Sulfur Fuel Oil | 380 CST HSFO |

Marine Gas Oil | MGO |

Distance | Dist |

Long Short−Term Memory | LSTM |

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Ship Feature | Value | Unit |
---|---|---|

Vessel Size | 3500~6700 | [AEU] |

Dead Weight | 13,000~19,000 | [Ton] |

Liftable Deck | 4~5 | [Unit] |

Ramp Capa | 50~200 | [Ton] |

Built | 1995~2017 | [Year] |

Variables | Abbreviation | Unit |
---|---|---|

Oil Price | Oil Price | USD |

Distance | Dist | Miles |

Speed | Speed | knot |

Sea Day | Sea Day | Day |

Port Day | Port Day | Day |

Duration = (Sea + Port day) | Duration | Day |

Bunker Cost | Bunker | USD |

Variable | Unit | Feature Characteristic | Value of Feature |
---|---|---|---|

Vessel type | AEU | Integer | 3500−6700 |

Speed | knot | Float | 0.59−93.31 |

Sea Day | Day | Float | 1.65−123.58 |

Duration | Day | Float | 4.33−409.76 |

Oil price | Day | Float | 26.60−125.45 |

Total bunker cost | USD | Float | 1026.12−3,007,958.65 |

Case | Routes | Type | Distance | Oil Price | Port Day |
---|---|---|---|---|---|

1 | ASNW | 6700 | 12,000 | 100 | 20 |

2 | ASNW | 6000 | 12,000 | ||

3 | ASAG | 6700 | 15,000 | ||

4 | ASAG | 6000 | 15,000 | ||

5 | ASEU | 6700 | 14,500 | ||

6 | ASEU | 6000 | 14,500 |

Variables | Minimum Value | Maximum Value |
---|---|---|

Speed | 0 | 50 |

Case | Best Speed | Best Bunker Cost |
---|---|---|

1 | 35.37 ± 1.02 | 609,739.25 ± 9024.13 |

2 | 34.30 ± 0.85 | 513,247.34 ± 5799.60 |

Case | Best Speed | Best Bunker Cost |
---|---|---|

3 | 34.80 ± 1.14 | 844,674.70 ± 6314.23 |

4 | 35.14 ± 1.03 | 789,084.20 ± 6067.58 |

Case | Best Speed | Best Bunker Cost |
---|---|---|

5 | 33.99 ± 0.98 | 846,517.50 ± 8893.67 |

6 | 35.37 ± 1.25 | 790,927.06 ± 4529.43 |

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## Share and Cite

**MDPI and ACS Style**

Su, M.; Su, Z.; Cao, S.; Park, K.-S.; Bae, S.-H.
Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships. *J. Mar. Sci. Eng.* **2023**, *11*, 1231.
https://doi.org/10.3390/jmse11061231

**AMA Style**

Su M, Su Z, Cao S, Park K-S, Bae S-H.
Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships. *Journal of Marine Science and Engineering*. 2023; 11(6):1231.
https://doi.org/10.3390/jmse11061231

**Chicago/Turabian Style**

Su, Miao, Zhenqing Su, Shengli Cao, Keun-Sik Park, and Sung-Hoon Bae.
2023. "Fuel Consumption Prediction and Optimization Model for Pure Car/Truck Transport Ships" *Journal of Marine Science and Engineering* 11, no. 6: 1231.
https://doi.org/10.3390/jmse11061231