Preliminary Study on CO2 Processing in CO2 Capture From Oxy-Fuel Combustion

2007 ◽  
Author(s):  
H. Li ◽  
J. Yan
Keyword(s):  
Energy Consumption ◽  
Co2 Capture ◽  
Minimum Energy ◽  
High Energy ◽  
Fuel Combustion ◽  
Energy Utilization ◽  
Excess Oxygen ◽  
Operating Parameters ◽  
Gas Processing ◽  
Minimum Energy Consumption

Oxy-fuel combustion is one of promising technologies for CO2 capture, which uses simple flue gas processing normally including compression, dehydration and purification/liquefaction (non-condensable gas separation). However relatively high levels of impurities in the flu gas present more challenges for the gas processing procedure. This paper studied the sensitivity of operating parameters to inlet composition, the effects of impurities on energy consumption, and the relationship between energy consumption and operating parameters. Results show that comparatively the total compression work is more sensitive to the composition of SO2 if the total mass flow is constant; while the operating temperature of purification is more sensitive to N2. To pursue the minimum energy consumption, from the viewpoint of impurity, the content of O2, N2, Ar and H2O should be lowered as much as possible, which means the amount of air leakage into the system and excess oxygen should be controlled at a low level in the combustion; as to SO2, if it is possible to co-deposit with CO2, its existence may be helpful to decrease compression work. From the viewpoint of operating parameters, low intermediate pressure, high intercooling temperature and high outlet pressure are favorable to achieve high energy utilization, if heat recovery is considered.

scholarly journals Maximizing Network Lifetime Operator for Wireless Sensor Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 267-272
Author(s):  
Dr. Deepali Virmani
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Topology Control ◽  
Performance Metrics ◽  
Minimum Energy ◽  
Energy Utilization ◽  
Wireless Sensor ◽  
Minimum Energy Consumption

Optimizing and enhancing network lifetime with minimum energy consumption is the major challenge in field of wireless sensor networks. Existing techniques for optimizing network lifetime are based on exploiting node redundancy, adaptive radio transmission power and topology control. Topology control protocols have a significant impact on network lifetime, available energy and connectivity. In this paper we categorize sensor nodes as strong and weak nodes based on their residual energy as well as operational lifetime and propose a Maximizing Network lifetime Operator (MLTO) that defines cluster based topology control mechanism to enhance network lifetime while guarantying the minimum energy consumption and minimum delay. Extensive simulations in Java-Simulator (J-Sim) show that our proposed operator outperforms the existing protocols in terms of various performance metrics life network lifetime, average delay and minimizes energy utilization.

Unscrambling the Building Envelope Insulation Technique in "Tianjin Energy Efficiency Design Standard for Rural Residential Buildings"

2013 ◽  
Vol 805-806 ◽  
pp. 1519-1523 ◽  
Author(s):  
Chang Feng Wang ◽  
Guo Qiang Fan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Minimum Energy ◽  
High Energy ◽  
Building Envelope ◽  
Minimum Energy Consumption ◽  
Tianjin City ◽  
Energy Efficiency Standard ◽  
Energy Efficiency Standards

In order to solve problems of high energy consumption and poor indoor thermal comfort in existing rural residential buildings, Tianjin city developed Tianjin energy efficiency standard for rural residential buildings, the building envelope insulation technique in the standard-including determination of heat transfer coefficient, principle of choosing insulation materials for building envelope, energy efficiency standards of walls, windows, and roofs are unscrambled particularly in this paper. It is suggested that natural materials and appropriate techniques are used to achieve the energy-saving goal for rural residential buildings with minimum energy consumption.

scholarly journals Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

2018 ◽  
Author(s):  
Hadi Abbas ◽  
Youngki Kim ◽  
Jason B. Siegel ◽  
Denise M. Rizzo
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Efficient Operation ◽  
Operating Modes ◽  
Minimum Energy Consumption ◽  
System A ◽  
Trip Time ◽  
Route Information

This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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