Energy-Efficient Train Control: Analysis of Local and Global Optimization

Review of energy-efficient train control and timetabling

We describe an oil and gas production optimization field application of a miscible gasflood for a cluster of oil reservoirs in South Oman. A portion of the produced sour gas from different reservoirs will be processed as sales gas and the extracted CO2 and H2S will be blended with the remaining produced gas stream, which will be injected back into selected reservoirs for miscible gas flooding. An integrated production system modeling (IPSM) methodology with a fully compositional (EOS, equation of state) description is used to couple the various reservoir models to a surface facilities model. A new element is a compositional gas blending optimization model in the gas processing facilities. This is the main source of non-convexity and non-linearity in the overall optimization model and requires a choice of local and global optimizers to uncover high quality solutions. What optimizer to use depends on the structure and targets of the gas blending problem. The produced oil and gas rates could be 10–20% below the optimum, especially when using local optimizers like SLP and SQP. Even for solutions where the fields produce the same oil and gas rates, and the blended sales and injection gas rates are the same, multiple (sub-)optimal solutions are still possible in the gas blending model by creating injection and sales gas with different compositions. Clear benefits of our IPSM methodology are that long term production forecasts were achieved while honoring all available facilities constraints such as those related to oil and gas production, the gas sweetening unit H2S and CO2 mole rates, injection compressor molecular weight, gas export specifications and facilities uptime. In addition, various forecast scenarios could quickly be generated to study the impact of different optimization objectives, e.g. high gas export vs. high oil production. Very important is also that all the constraints and objectives are contained in a single input file for running the integrated subsurface-surface model. This makes auditing the various forecast scenarios very transparent. Introduction A large number of gas and oil fields are contaminated by significant amounts of CO2 and H2S. Separation of CO2 and H2S from the hydrocarbon gas stream is expensive and leads to disposal problems. However, one way to use these contaminants is to re-inject them back into the oil reservoirs for enhanced oil recovery (EOR) by miscible gas flooding, which can result in very high recovery factors. Depending on the requirements of individual reservoirs for miscible conditions, the extracted H2S and CO2 can be blended in various ratios with produced sour hydrocarbon gas. Such oil production and gas blending optimization problems are challenging when producing from a portfolio of reservoirs with different compositions, pressures and temperatures. In this paper, we present the integrated oil and gas production optimization of a miscible gasflood in South Oman. The carbonate reservoirs are about 100 m thick and located at a depth of 3.5 to 5.5 km. There are several subsurface uncertainties, such as the presence of (sealing) faults, or "thief zones", which are higher permeable layers in low permeable rocks. These may cause the gasflood not to perform at all, or lead to early gas breakthrough. To minimize the risk, development of the oil reservoirs is done in a phased manner. A miscible gasflood EOR scheme also requires abundant availability of injection gas and this is provided by a nearby gas field. How the gas can be blended is described by Deinum et al.1. The various subsurface reservoirs are modeled with the in-house MoReS reservoir simulator in compositional mode and linked to a fit for purpose compositional surface facilities model in the Hydrocarbon Field Planning Tool (HFPT)2,3 a Shell proprietary tool for integrated subsurface-surface modeling. The simulations are run efficiently and robustly using an adaptive implicit non-linear solver with a fast PVT flash method. The well equations in MoReS, including those of the separator train, are solved fully implicitly, whereas the coupling to the facilities is treated explicitly. As gas blending problems are strongly non-linear and non-convex, HFPT has a choice of local and global optimization algorithms to deal with the gas production and blending optimization to produce the sales and injection gas.

On optimization of the RBF shape parameter in a grid-free local scheme for convection dominated problems over non-uniform centers

Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based local optimization approach

One promising method of improving Evolutionary Algorithm (EA) performance is to improve its fine tuning capabilities by using an additional local optimization operator in the evolutionary cycle. This customization on the traditional EA is typically called a Memetic Algorithm (MA). Adding the appropriate local optimization algorithm can increase performance, while a poor choice can decrease performance. Thus, some knowledge is required to select the correct algorithm. In many cases the optimal algorithm selection is not known and it may not be static, but could change during evolution. This investigation combines a method of local optimizer evolution using Push Genetic Programming with a method of automatic, self-configuration called Supportive Coevolution. This combination creates a novel MA that coevolves local optimization operators with target fitness function solution candidates. Implementation methodology is shown and experimentation details with corresponding results are presented. Some additional parameters that were discovered for performance tuning are discussed along with a study of their impact on the algorithm's performance. Discussion of some interesting insights followed by some suggestions for further investigation are also provided. Results show the proposed technique can improve the performance of an EA by providing automatically configured, coevolved local optimization operators to a MA.

This study develops a time-driven train performance simulation (TPS) model consisting of train traction module, track alignment module, and train control module, which is applied to emulate the movement of a train, calculate energy consumption, and estimate travel time, considering various vertical track alignments and operational controls. A numerical example is given in this study to demonstrate the features of the developed TPS, such as generating time varying operational measures (e.g., the tractive effort, resistance, acceleration rate, speed, and energy consumption, etc.) and calculating energy consumption and travel time. The most energy efficient train control, or called “golden run,” associated with various track alignments, speed limits, and schedule adherence are identified, while the impact of coasting locations to travel time and energy consumption is also assessed. A sensitivity analysis is conducted, and the relationship between model parameters and control variables are discussed.

Energy Efficient Motion Control for a Light Rail Vehicle Using The Big Bang Big Crunch Algorithm

Identifying the optimal switching point between different invasive alien species (IAS) management policies is a very complex task and policy makers are in need of modelling tools to assist them. In this paper we develop an optimal control bioeconomic model to estimate the type of optimal policy and switching point of control efforts against a spreading IAS. We apply the models to the case study of Colorado potato beetle in the UK. The results demonstrate that eradication is optimal for small initial sizes of invasion at discovery. High capacity of the agency to reduce spread velocity for several years leads to smaller total overall costs of invasion and makes eradication optimal for larger sizes of initial invasion. In many cases, it is optimal to switch from control to acceptance within the time horizon. The switching point depends on the capacity of the agency, initial size of invasion, spread velocity of the IAS and the ratio of unit cost of damage and removal. We encourage the integration of the dispersal patterns of the invader and the geometry of the invasion in the theoretical development of the economics of IAS invasion management.

Deterministic chaos situations produced by local optimisation algorithms are considered. A family of test multi‐extremal functions is proposed that allows getting the collections of functions with an arbitrary pre‐defined number of the local optima. Special software is developed for generating test functions from the family, performing multiple local optimisations of different initial points, visualising the chaotic attraction regions. Properties of the regions of attraction are presented. Regularity of the attraction regions is required to be a special characteristic of the local optimisation algorithms.

In this work, optimization methodologies of the kinematics of flapping wings in forward and hover flights are considered. Particularly, local and global optimization algorithms are combined with the unsteady vortex lattice method (UVLM) to determine the most efficient kinematics. In the first problem, the kinematic optimization of a three-dimensional flapping wing in forward flight with active shape morphing is aimed at maximizing the propulsive efficiency under lift and thrust constraints. Results show that due to the quasi-concavity of the objective function associated with the flapping kinematics employed in forward flight, local gradient-based optimizers perform very well in terms of accuracy and computational cost. In the second problem, the objective is to identify the optimized kinematics of a twodimensional hovering wing that minimize the aerodynamic power under lift constraint. The results show that using a hybrid optimization algorithm (combination of local and global schemes) accelerates the convergence to optimal points and avoids being trapped at local minimum points. While the efficiency of using a local optimizer, a global optimizer, or a hybrid of the two depends on the nature of the problem and associated design space, it is determined that hybrid optimization is best suited for determining local minima.

*† ‡ § An efficient and high-fidelity design approach is proposed by combining global and local optimization methods for wing planform and surface design. For enhanced design results, aerodynamic shape optimization process is carried out via 2-stage with different optimization strategy. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with high DOF (Degree of Freedom) geometric change. For global optimization, meta-modeling techniques such as RS (Response Surface) and Kriging methods are used in conjunction with Genetic Algorithm (GA). For local optimization, a discrete adjoint variable method is used. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The capability of proposed design framework including wing planform design variables can be evaluated by the drag decomposition method which can provide improvement of induced drag and wave drag, respectively.

This work presents an enhanced polar scan-matching procedure (E-PSM) that obtains its inputs from the application of an advanced line segmentation algorithm to the laser range returns. Additionally, a set of alternative methods based on local and global optimization algorithms is introduced. Results from robot simulation tests are provided for different ranges of laser range return noise and odometry sensor error levels. The results show that the proposed E-PSM algorithm and one of the methods based on global optimization yield good robot pose estimation precision while keeping computational costs at a reasonable level.

This paper investigates optimal power management in dc microgrids, with its applications to dc dual-source trolleybus systems. High mobility of buses and their impact on power supply networks introduce challenging power management issues. This paper incorporates line power losses in power management strategies and introduces a new distributed optimal power management methodology. A multi-objective optimization model is developed. Using only neighborhood information exchange among feeder lines in the network, the new consensus-type control accommodates both feeder current allocation and power loss reduction with fast convergence. One critical finding of this paper is that our local recursive optimization algorithms achieve the global optimal solution asymptotically. Under random noise on information exchange, convergence and optimality of the proposed method are established rigorously. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Feasibility, accuracy, and comparison with global optimization results are demonstrated.

An external contractor receives a payment from a manufacturer for periodically performing preventive maintenance and for performing minimal repairs whenever process failures occur. Suppose a new technology will be available in the future, but the timing is uncertain. We propose specifying one or multiple time points in a maintenance contract, at which the manufacturer can adopt (switch to) the new technology, if it becomes available, and change the preventive maintenance schedule for the remaining time in the contract period. A model is developed to determine the value of using these (switch) points in a maintenance contract to the manufacturer. The numerical results show that, with a higher arrival rate of the new technology, the value becomes greater, and thus, the optimal switch point should be set earlier. Furthermore, if the new technology reduces maintenance costs, using switch points adds more value to a cost-plus-margin method than either the fixed payment or cost-plus-fixed-fee methods. On the other hand, if adopting the new technology results in higher maintenance costs, the value using switch points is higher for the fixed payment method.

Discontinuously electrified sections, such as neutral sections (NSs) widely exist in modern electrified railways. As a special arrangement of insulator, NSs with no electricity supplies are set up to ensure the two sections are kept electrically separate. This paper proposes a distance‐based mixed‐integer linear programming (MILP) model to incorporate both NS location planning and energy‐efficient train control (EETC) problem and concurrently optimise the NS location and train speed trajectory. The main contribution of this study is that based on the proposed integrated model, a number of case studies are conducted to investigate on the impact mechanism of NS locations on the total energy consumption of train operations. The optimisation results show that the energy saving rate in comparison with the worst cases is ranging from 1.9% to 6.1%% in various scenarios and significant saving rate can be achieved via planning the NS to be located in coasting areas as determined by EETC. In conclusion, the energy‐saving effect of the optimal NS location planning on the total energy consumption largely depends on how the NS‐triggered forced coasting is located in the journey and NS locations near stations and switching areas of speed limit lead to significant energy increase for bi‐directional journeys.