Minimum Energy Consumption in Multicomponent Distillation. 1.VminDiagram for a Two-Product Column

2003 ◽  
Vol 42 (3) ◽  
pp. 596-604 ◽  
Author(s):  
Ivar J. Halvorsen ◽  
Sigurd Skogestad
Keyword(s):  
Energy Consumption ◽  
Minimum Energy ◽  
Minimum Energy Consumption ◽  
Multicomponent Distillation ◽  
Product Column

Minimum Energy Consumption in Multicomponent Distillation. 2. Three-Product Petlyuk Arrangements

2003 ◽  
Vol 42 (3) ◽  
pp. 605-615 ◽  
Author(s):  
Ivar J. Halvorsen ◽  
Sigurd Skogestad
Keyword(s):  
Energy Consumption ◽  
Minimum Energy ◽  
Minimum Energy Consumption ◽  
Multicomponent Distillation

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.

A multicast delivery approach with minimum energy consumption for wireless multi-hop networks

2015 ◽  
Vol 62 (4) ◽  
pp. 771-782 ◽  
Author(s):  
Dingde Jiang ◽  
Zhengzheng Xu ◽  
Zhihan Lv
Keyword(s):  
Energy Consumption ◽  
Minimum Energy ◽  
Minimum Energy Consumption ◽  
Delivery Approach

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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