Fuel Optimization Using Biologically-Inspired Computational Models

2008 ◽  
Author(s):  
Thamar E. Mora ◽  
Abu B. Sesay ◽  
Jo¨rg Denzinger ◽  
H. Golshan ◽  
G. Poissant ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Computational Models ◽  
State Of The Art ◽  
Optimization Method ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Biologically Inspired ◽  
Natural Gas Pipeline ◽  
Constraints Solving ◽  
Fuel Optimization

This paper presents a method for optimizing the fuel consumption of large and complex natural gas pipeline systems. The optimization method uses a biologically-inspired computational model, namely Particle Swarm Systems. The main objective is to identify the set of operating conditions that minimizes the use of fuel in compressor stations while maintaining the desired throughput and satisfying given system constraints. Solving this fuel optimization problem is non-trivial given the large number of decision variables and constraints in large networks, the nature of the fuel function and the minimum response time imposed by the frequent changes in flow nominations. The experimental evaluation tested on various subnetworks of TransCanada show that the proposed optimization approach meets TransCanada’s time requirements and reliably outperforms the interactive method that is the current state-of-the-art by providing solutions for which the fuel consumption is 12% less than state-of-the-art methods.

Optimal control of a turbocharged direct injection diesel engine by direct method optimization

2018 ◽  
Vol 20 (6) ◽  
pp. 640-652 ◽  
Author(s):  
Jose Manuel Luján ◽  
Carlos Guardiola ◽  
Benjamín Pla ◽  
Alberto Reig
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Fuel Efficiency ◽  
Optimization Method ◽  
Operating Conditions ◽  
Experimental Results ◽  
Pollutant Emissions ◽  
Engine Fuel ◽  
Optimal Control Strategy

This work studies the effect and performance of an optimal control strategy on engine fuel efficiency and pollutant emissions. An accurate mean value control-oriented engine model has been developed and experimental validation on a wide range of operating conditions was carried out. A direct optimization method based on Euler’s collocation scheme is used in combination with the above model in order to address the optimal control of the engine. This optimization method provides the optimal trajectories of engine controls (fueling rate, exhaust gas recirculation valve position, variable turbine geometry position and start of injection) to reproduce a predefined route (speed trajectory including variable road grade), minimizing fuel consumption with limited [Formula: see text] emissions and a low soot stamp. This optimization procedure is performed for a set of different [Formula: see text] emission limits in order to analyze the trade-off between optimal fuel consumption and minimum emissions. Optimal control strategies are validated in an engine test bench and compared against engine factory calibration. Experimental results show that significant improvements in both fuel efficiency and emissions reduction can be achieved with optimal control strategy. Fuel savings at about 4% and less than half of the factory [Formula: see text] emissions were measured in the actual engine, while soot generation was still low. Experimental results and optimal control trajectories are thoroughly analyzed, identifying the different strategies that allowed those performance improvements.

DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts

2019 ◽  
Vol 34 (04) ◽  
pp. 1950032 ◽  
Author(s):  
Gaurav Dhiman ◽  
Pritpal Singh ◽  
Harsimran Kaur ◽  
Ritika Maini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
State Of The Art ◽  
Real Life ◽  
Optimization Method ◽  
Optimization Approach ◽  
Effective Number ◽  
Number Of Solutions ◽  
Efficient Prediction ◽  
Competing Models

This paper presents a new model using optimization approach for efficient prediction of load in real-life environment. Monte Carlo simulation and Schrödinger equations provide the effective number of solutions. This technique is useful in representation of relationships between different models. The proposed algorithm is verified and validated with various state-of-the-art approaches for solving economic load power dispatch problem to demonstrate its efficiency. Experimental results signify that the proposed algorithm is more precise than existing competing models.

scholarly journals A Feature Optimization Approach Based on Inter-Class and Intra-Class Distance for Ship Type Classification

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5429
Author(s):  
Chen Li ◽  
Ziyuan Liu ◽  
Jiawei Ren ◽  
Wenchao Wang ◽  
Ji Xu
Keyword(s):  
Deep Learning ◽  
Objective Function ◽  
State Of The Art ◽  
Optimization Method ◽  
Classification Algorithms ◽  
Optimization Approach ◽  
Time Frequency ◽  
Real Environment ◽  
Feature Optimization ◽  
Type Classification

Deep learning based methods have achieved state-of-the-art results on the task of ship type classification. However, most existing ship type classification algorithms take time–frequency (TF) features as input, the underlying discriminative information of these features has not been explored thoroughly. This paper proposes a novel feature optimization method which is designed to minimize an objective function aimed at increasing inter-class and reducing intra-class feature distance for ship type classification. The objective function we design is able to learn a center for each class and make samples from the same class closer to the corresponding center. This ensures that the features maximize underlying discriminative information involved in the data, particularly for some targets that usually confused by the conventional manual designed feature. Results on the dataset from a real environment show that the proposed feature optimization approach outperforms traditional TF features.

Energy Usage in Natural Gas Pipeline Applications

2011 ◽  
Vol 134 (2) ◽  
Author(s):  
Augusto Garcia-Hernandez ◽  
Klaus Brun
Keyword(s):  
Natural Gas ◽  
Fuel Consumption ◽  
Transport System ◽  
Gas Pipeline ◽  
Operating Conditions ◽  
Pipeline System ◽  
Entire System ◽  
Energy Usage ◽  
Natural Gas Pipeline

Energy required to transport the fluid is an important parameter to be analyzed and minimized in pipeline applications. However, the pipeline system requirements and equipment could impose different constraints for operating pipelines in the best manner possible. One of the critical parameters that it is looked at closely, is the machines’ efficiency to avoid unfavorable operating conditions and to save energy costs. However, a compression-transport system includes more than one machine and more than one station working together at different conditions. Therefore, a detailed analysis of the entire compression system should be conducted to obtain a real power usage optimization. This paper presents a case study that is focused on analyzing natural gas transport system flow maximization while optimizing the usage of the available compression power. Various operating scenarios and machine spare philosophies are considered to identify the most suitable conditions for an optimum operation of the entire system. Modeling of pipeline networks has increased in the past decade due to the use of powerful computational tools that provide good quality representation of the real pipeline conditions. Therefore, a computational pipeline model was developed and used to simulate the gas transmission system. All the compressors’ performance maps and their driver data such as heat rate curves for the fuel consumption, site data, and running speed correction curves for the power were loaded in the model for each machine. The pipeline system covers 218 miles of hilly terrain with two looped pipelines of 38″ and 36″ in diameter. The entire system includes three compressor stations along its path with different configurations and equipment. For the optimization, various factors such as good efficiency over a wide range of operating conditions, maximum flexibility of configuration, fuel consumption and high power available were analyzed. The flow rate was maximized by using instantaneous maximum compression capacity at each station while maintaining fixed boundary conditions. This paper presents typical parameters that affect the energy usage in natural gas pipeline applications and discusses a case study that covers an entire pipeline. A modeling approach and basic considerations are presented as well as the results obtained for the optimization.

Energy Usage in Natural Gas Pipeline Applications

2011 ◽  
Author(s):  
Augusto Garcia-Hernandez ◽  
Klaus Brun
Keyword(s):  
Natural Gas ◽  
Fuel Consumption ◽  
Transport System ◽  
Gas Pipeline ◽  
Operating Conditions ◽  
Pipeline System ◽  
Entire System ◽  
Energy Usage ◽  
Natural Gas Pipeline

Energy required to transport the fluid is an important parameter to be analyzed and minimized in pipeline applications. However, the pipeline system requirements and equipment could impose different constraints for operating pipelines in the best manner possible. One of the critical parameters that is looked at closely, is the machines’ efficiency to avoid unfavorable operating conditions and to save energy costs. However, a compression-transport system includes more than one machine and more than one station working together at different conditions. Therefore, a detailed analysis of the entire compression system should be conducted to obtain a real power usage optimization. This paper presents a case study that is focused on analyzing natural gas transport system flow maximization while optimizing the usage of the available compression power. Various operating scenarios and machine spare philosophies are considered to identify the most suitable conditions for an optimum operation of the entire system. Modeling of pipeline networks has increased in the past decade due to the use of powerful computational tools that provide good quality representation of the real pipeline conditions. Therefore, a computational pipeline model was developed and used to simulate the gas transmission system. All the compressors’ performance maps and their driver data such as heat rate curves for the fuel consumption, site data, and running speed correction curves for the power were loaded in the model for each machine. The pipeline system covers 218 miles of hilly terrain with two looped pipelines of 38″ and 36″ in diameter. The entire system includes three compressor stations along its path with different configurations and equipment. For the optimization, various factors such as good efficiency over a wide range of operating conditions, maximum flexibility of configuration, fuel consumption and high power available were analyzed. The flow rate was maximized by using instantaneous maximum compression capacity at each station while maintaining fixed boundary conditions. This paper presents typical parameters that affect the energy usage in natural gas pipeline applications and discusses a case study that covers an entire pipeline. A modeling approach and basic considerations are presented as well as the results obtained for the optimization.

scholarly journals Customer-Oriented Vehicle Routing Problem with Environment Consideration: Two-Phase Optimization Approach and Heuristic Solution

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Fanting Meng ◽  
Yong Ding ◽  
Wenjie Li ◽  
Rongge Guo
Keyword(s):  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Optimization Method ◽  
Second Phase ◽  
Optimization Approach ◽  
Two Phase ◽  
Routing Problem ◽  
Problem Model ◽  
Phase Optimization

With the fastest consumer demand growth, the increasing customer’s demands trend to multivarieties and small-batch and the customer requires an efficient distribution planning. How to plan the vehicle route to meet customer satisfaction of mass distribution as well as reduce the fuel consumption and emission has become a hot topic. This paper proposes a two-phase optimization method to handle the vehicle routing problem, considering the customer demands and time windows coupled with multivehicles. The first phase of the optimization method provides a fuzzy hierarchical clustering method for customer grouping. The second phase formulates the optimization en-group vehicle routing problem model and a genetic algorithm to account for vehicle routing optimization within each group so that fuel consumption and emissions are minimized. Finally, we provide some numerical examples. Results show that the two-phase optimization method and the designed algorithm are efficient.

scholarly journals Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search

2018 ◽  
Author(s):  
Yi-Qi Hu ◽  
Yang Yu ◽  
Zhi-Hua Zhou
Keyword(s):  
State Of The Art ◽  
Optimization Method ◽  
Parameter Selection ◽  
Optimization Approach ◽  
Network Architectures ◽  
Directional Model ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Parameter Search ◽  
Difficult Issue

Hyper-parameter selection is a crucial yet difficult issue in machine learning. For this problem, derivative-free optimization has being playing an irreplaceable role. However, derivative-free optimization commonly requires a lot of hyper-parameter samples, while each sample could have a high cost for hyper-parameter selection due to the costly evaluation of a learning model. To tackle this issue, in this paper, we propose an experienced optimization approach, i.e., learning how to optimize better from a set of historical optimization processes. From the historical optimization processes on previous datasets, a directional model is trained to predict the direction of the next good hyper-parameter. The directional model is then reused to guide the optimization in learning new datasets. We implement this mechanism within a state-of-the-art derivative-free optimization method SRacos, and conduct experiments on learning the hyper-parameters of heterogeneous ensembles and neural network architectures. Experimental results verify that the proposed approach can significantly improve the learning accuracy within a limited hyper-parameter sample budget.

scholarly journals Contributions on experimental research of fuel consumption and management system in K9K892 Diesel engine from Dacia-Renault

2018 ◽  
Vol 184 ◽  
pp. 01018
Author(s):  
Doru Baldean ◽  
Adela-Ioana Borzan
Keyword(s):  
Experimental Data ◽  
Experimental Study ◽  
Economic Development ◽  
Diesel Engine ◽  
Fuel Consumption ◽  
Management System ◽  
State Of The Art ◽  
Operating Regime ◽  
Operating Conditions ◽  
Socio Economic Development

The present paper develops an experimental study that highlights some aspects of fuel consumption in engine's cylinders in order to outline the influence of engine's management system and operating conditions upon economy and fuel consumption, with corresponding effect on sustainability and adequate socio-economic development. The present work makes a theoretical and applied enquiry in the system features from Euro 5 diesel engine management in relation with fuel consumption and different driving scenarios. There were closely monitored engine temperatures, driving stiles and the values displayed ON-BOARD-DIAGNOSIS screens. The importance and opportunity for experimental inquiry of the fuel consumption and economy problems in compression ignited engine resides in the state of the art equipment and managing systems available today for monitoring all the engine's activities and for making possible to outline the economical operating regime, in order to reduce undesired losses. The experimental data are analyzed in detail.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Molecular Docking ◽  
Computer Calculation ◽  
Molecular Graph ◽  
Optimization Method ◽  
Docking Studies ◽  
Optimization Approach ◽  
Qsar Modeling ◽  
Monte Carlo Optimization ◽  
Molecular Docking Studies

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

Optimization for speed regulation strategy of compound coupled hydro-mechanical transmission

2020 ◽  
pp. 095440702098056
Author(s):  
Jin Yu ◽  
Pengfei Shen ◽  
Zhao Wang ◽  
Yurun Song ◽  
Xiaohan Dong
Keyword(s):  
Fuel Consumption ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Speed Ratio ◽  
Mechanical Transmission ◽  
Speed Regulation ◽  
Ratio Change ◽  
Low Efficiency ◽  
Wheel Loaders

Heavy duty vehicles, especially special vehicles, including wheel loaders and sprinklers, generally work with drastic changes in load. With the usage of a conventional hydraulic mechanical transmission, they face with these problems such as low efficiency, high fuel consumption and so forth. Some scholars focus on the research to solve these issues. However, few of them take into optimal strategies the fluctuation of speed ratio change, which can also cause a lot of problems. In this study, a novel speed regulation is proposed which cannot only solve problems above but also overcome impact caused by speed ratio change. Initially, based on the former research of the Compound Coupled Hydro-mechanical Transmission (CCHMT), the basic characteristics of CCHMT are analyzed. Besides, to solve these problems, dynamic programming algorithm is utilized to formulate basic speed regulation strategy under specific operating condition. In order to reduce the problem caused by speed ratio change, a new optimization is applied. The results indicate that the proposed DP optimal speed regulation strategy has better performance on reducing fuel consumption by up to 1.16% and 6.66% in driving cycle JN1015 and in ECE R15 working condition individually, as well as smoothing the fluctuation of speed ratio by up to 12.65% and 19.01% in those two driving cycles respectively. The processes determining the speed regulation strategy can provide a new method to formulate the control strategies of CCHMT under different operating conditions particularlly under real-world conditions.

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