低碳经济下的港口泊位分配模型及其算法实现

航运公司正在进行前所未有的努力以减少船舶的燃油消耗量及碳排放量,而港口所制定的泊位分配计划对于船舶的油耗量和碳排放量有着直接的影响。由于船舶的到港时间是港方制定泊位分配计划的关键参数,因此将船舶到港时间作为决策变量引入传统的泊位分配（BAP）模型中,设计了港口与船方协调调度的新的泊位分配策略--VAT（Variable Arrival Time）策略,同时将船舶油耗和碳排放量融入BAP 模型的目标函数中,建立了船舶油耗量最小和船舶离港延迟时间最短的双目标优化模型。采用多目标遗传算法对该模型进行求解,并用仿真算例验证了该策略的有效性。计算结果表明,VAT策略可以大大削减航运公司的燃油消耗和船舶的碳排放,同时可以提高港口的服务水平,缩短船舶在港等待时间。     

基于多目标遗传算法的柴电混合动力船舶功率分配优化

船舶在传统的柴油机推进模式时,低负荷下船用主机性能不佳,燃油消耗率高,燃烧质量差,在提高经济指标和减少排放指标方面遇到瓶颈。柴-电动混合动力推进形式能够通过合理的分配,有效降低燃料消耗和排放。针对混合动力船舶的动力结构,构建关于油耗和污染物排放的多目标优化模型。采用多目标遗传算法(NSGA-II)优化功率在主机和发电机间的分配问题,旨在所有目标之间进行折衷处理,尽量达到Pareto最优。选取某深水三用工作船的典型负荷进行优化计算和仿真验证,仿真结果表明优化后的结果能够兼顾排放性和经济性,提高船舶能效。     

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energy-intensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes.     

信息作战环境下舰船无源定位系统设计与实现

信息作战环境下,对我方舰船位置坐标与敌方舰船所在地理位置坐标进行准确、快速定位是定位系统设计的必备条件;传统舰船上使用的舰船定位系统存在定位源信号追踪稳定性差、恶劣环境下的定位信号搜索域范围小、定位波反干扰性差等一系列的问题;通过深度的研究分析,提出信息作战环境下舰船无源定位系统设计;采用三维立体空间定位单元、广域GPNS信号检索模组与谐波振频反干扰技术,实现作战环境下舰船无源定位系统设计;对作战环境下舰船无源定位系统的设计进行仿真实验测试;仿真实验测试证明,提出的信息作战环境下舰船无源定位系统设计具有定位速度快、坐标定位准、定位源抗干扰性强的优点,满足当今恶劣的信息作战环境,能够达到无源定位的目的。     

A multi-level optimization technique based on fuel consumption and energy index in early-stage ship design

Reducing lifetime fuel consumption (LFC) and energy efficiency design index (EEDI) are two of the main concerns of shipping industry in recent years. This paper presents a multi-disciplinary and multi-level optimization scheme-based software (HPS-MOP2) to design a hull–propeller system simultaneously from the LFC and EEDI point of view in early-stage ship design. Calculations of the ship resistance and propeller performance are essential to optimize the ship hull–propeller system. Two numerical methods with variable fidelity, non-uniform rational basis spline (NURBS) geometry modelling technique and new version of multi-objective evolutionary algorithm based on decomposition (MOEA/D) are three main parts of the proposed methodology. A bulk carrier propelled by a well-known propeller is used as a base model in three different study cases based on specific fuel oil consumption (SFOC) curves provided by the engine manufacturers Wartsila, MAN and Caterpillar. The presented results illustrate that the employed approach may achieve cost- and energy-efficient designs.

     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Federated learning for green shipping optimization and management

Many shipping companies are unwilling to share their raw data because of data privacy concerns. However, certain problems in the maritime industry become much more solvable or manageable if data are shared—for instance, the problem of reducing ship fuel consumption and thus emissions. In this study, we develop a two-stage method based on federated learning (FL) and optimization techniques to predict ship fuel consumption and optimize ship sailing speed. Because FL only requires parameters rather than raw data to be shared during model training, it can achieve both information sharing and data privacy protection. Our experiments show that FL develops a more accurate ship fuel consumption prediction model in the first stage and thus helps obtain the optimal ship sailing speed setting in the second stage. The proposed two-stage method can reduce ship fuel consumption by 2.5%–7.5% compared to models using the initial individual data. Moreover, our proposed FL framework protects the data privacy of shipping companies while facilitating the sharing of information among shipping companies.     

Modelling of ship engine exhaust emissions in ports and extensive coastal waters based on terrestrial AIS data – An Australian case study

A model is developed to calculate and spatially allocate ship engine exhaust emissions in ports and extensive coastal waters using terrestrial Automatic Identification System data for ship movements and operating modes. The model is applied to the Australian region. The large geographical extent and number of included ports and vessels, and anomalies in the AIS data are challenging. Particular attention is paid to filtering of the movement data to remove anomalies and assign correct operating modes. Data gaps are filled by interpolation and extrapolation. Emissions and fuel consumption are calculated for each individual vessel at frequent intervals and categorised by ship type, ship size, operating mode and machinery type. Comparisons of calculated port emissions with conventional inventories and ship visit data are favourable. Estimations of ship emissions from regions within a 300 km radius of major capital cities suggest that a non-negligible percentage of air pollutants may come from ships.     

改进的数据驱动子空间算法求解钢铁企业能源预测问题

本文以钢铁企业生产与能源系统作为研究背景,设计一种数据驱动的子空间方法(data-driven subspace,DDS)预测各生产工序的能源消耗.针对钢铁生产中能源消耗和回收的特点进行了分析,以提取子空间方法的建模因素;为了设计有效的求解方法,对实际生产和数据的特征进行了分析.为了提高预测准确率,文中引入了反馈因子和遗忘因子来改进子空间方法,因子的取值采用粒子群算法(particle swarm optimization,PSO)来优化.对实际生产数据的测试验证了本文所提出的方法的有效性,该结果能够为钢铁企业的能源预测和管理提供有效的决策支持.     

A preliminary study on learning effect in a contact positioning task in a virtual environment

How the human central nervous system adjusts sensorimotor coordination to haptic stimuli is a major topic in several fields including robotics. There are studies focusing on contact positioning tasks such as catching a ball or a volley in tennis, where position control of a human hand is required only at the moment of contact for successful completion of the tasks. A previous study dealt with a task of catching a ball in a virtual environment, and reported an interesting observation of the learning process in the relationship between operational performance and energy consumption of an operator; however, the operational condition given to the operators in the previous study seemed to be relatively simple. The interest in this paper is similar to that of the previous study; however, this paper considers application of a slightly more complex operational condition to a task of catching a ball. For corresponding to increase of difficulty in operation in the task, two movements of a paddle for catching a ball are given to operators, and difference between the movements is observed. Although the final goal of the study in this paper is similar to that in the previous study, this paper considers how learning effect can be observed in operational performance by comparing the performance of novice operators with that of experienced operators. The comparison shows differences in the performances between the operators from three analytic aspects. This paper also considers how many operations are required for novice operators to exhibit the learning effect in operational performance.     

The effect of roll-stabilisation controllers on warship operational performance

At present, most ships are stabilised for roll using active fins controlled by a PID controller developed using classical control theory and these appear perfectly adequate for most warships. However, what is not known is how the choice of controller for a fin-stabilisation system affects the operational performance of the ship. This paper illustrates the potential improvements in the ability of a warship to perform an anti-submarine warfare mission in the North Atlantic when using different control strategies for fin-roll stabilisation. Also included is an assessment of the potential benefits to operational performance of an integrated fin rudder roll stabilisation system.     

船舶典型结构构件损耗因子试验研究

损耗因子是衡量系统阻尼特性并决定其振动能量耗散能力的重要参数,对船舶振动噪声传递和预报分析都具有重要影响。针对船舶结构特点,选择横舱壁板架、舭部外板板架和船底外板板架等舰船典型结构建立试验模型,采用瞬态衰减法对舰船典型结构损耗因子进行测试,并分析舰船典型结构构件损耗因子变化规律。通过测试典型结构构件损耗因子,为舰船结构噪声数值计算方法提供系统参数输入,也为舰船典型结构损耗因子数据库提供初步积累。     

Simulation environment for the investigation of automatized cooperation of marine crafts

There are marine operations involving the cooperation of several marine crafts. For instance spill-over confinement, rescue, towing, etc. A simulation environment has been developed to analyse the cooperation problems that may arise along an operation, in order to develop procedures for better joint work of the ships. The final target is to add some automatization into the control of the ships, with on-board computers helping the ship captains to take the right decisions. The environment considers the ships as agents. The paper introduces the simulation environment and describes some examples concerning ship formation cases.     

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.     

Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems.     

Full-shipload tramp ship routing and scheduling with variable speeds

This paper investigates the simultaneous optimization problem of routing and sailing speed in the context of full-shipload tramp shipping. In this problem, a set of cargoes can be transported from their load to discharge ports by a fleet of heterogeneous ships of different speed ranges and load-dependent fuel consumption. The objective is to determine which orders to serve and to find the optimal route for each ship and the optimal sailing speed on each leg of the route so that the total profit is maximized. The problem originated from a real-life challenge faced by a Danish tramp shipping company in the tanker business. To solve the problem, a three-index mixed integer linear programming formulation as well as a set packing formulation is presented. A novel Branch-and-Price algorithm with efficient data preprocessing and heuristic column generation is proposed. The computational results on the test instances generated from real-life data show that the heuristic provides optimal solutions for small test instances and near-optimal solutions for larger test instances in a short running time. The effects of speed optimization and the sensitivity of the solutions to the fuel price change are analyzed. It is shown that speed optimization can improve the total profit by 16% on average and the fuel price has a significant effect on the average sailing speed and total profit.     

Reduced Carbon and Energy Footprint in Highway Operations: The Highway Energy Assessment (HERA) Methodology

Global demand for mobility is increasing and the environmental impact of transport has become an important issue in transportation network planning and decision-making, as well as in the operational management phase. Suitable methods are required to assess emissions and fuel consumption reduction strategies that seek to improve energy efficiency and furthering decarbonization. This study describes the development and application of an improved modeling framework – the HERA (Highway EneRgy Assessment) methodology – that enables to assess the energy and carbon footprint of different highways and traffic flow scenarios and their comparison. HERA incorporates an average speed consumption model adjusted with a correction factor which takes into account the road gradient. It provides a more comprehensive method for estimating the footprint of particular highway segments under specific traffic conditions. It includes the application of the methodology to the Spanish highway network to validate it. Finally, a case study shows the benefits from using this methodology and how to integrate the objective of carbon footprint reductions into highway design, operation and scenario comparison.     

Fuzzy decision support system for ship lock control

This paper presents the development of a Decision Support System (DSS) for the management of ship locks that relies on fuzzy logic. It contains a brief overview of the history and the construction of locks and basic information related to fuzzy logic, fuzzy linguistic variables and methods used in approximate reasoning. In reality, ship lock control is mostly based on the subjective estimations and the experience of a lock master (ship lock operator). The fuzzy set theory is the most favourable mathematical approach for consideration of indefiniteness and subjective estimates. This paper analyses the control process of a ship lock on a two-way waterway, with one chamber designed for one vessel. A control algorithm is constructed according to a set of linguistic rules that describes the operator’s control strategy. The subjective estimations are therefore implemented in the algorithm as fuzzy sets. Fuzzy rules aggregate the final fuzzy set and defuzzification produces a decision. A set of ship traffic data is generated for analysis and simulation purposes based on the annual distribution of ship arrivals at the lock. Two criteria are presented and used in parallel with the Fuzzy DSS (FDSS). These two extreme criteria reflect the interests of shippers on one side and workers and owners of the lock on the other side. These interests occur in actual systems and are used here to evaluate the results obtained using the FDSS. This paper additionally describes the design of the SCADA (Supervisory Control And Data Acquisition) software. This software relies on a PLC (Programmable Logic Controller) and provides a platform on which to implement the desired fuzzy algorithm. The software was developed with the suggestions of operators who have extensive experience in ship lock control. The presented control system can be used for support in decision making in control processes and in the training of new operators of ship locks.     

Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions

Diesel engines are fuel efficient which benefits the reduction of CO₂ released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low