scholarly journals OPTIMIZATION OF BURNING PRODUCTION PROCESS OF CERAMSITE WITH SPECIFIED DENSITY

2017 ◽  
Vol 3 ◽  
pp. 57 ◽  
Author(s):  
K. S. Galitskov ◽  
O. V. Samokhvalov ◽  
A. S. Fadeev
Keyword(s):  
Energy Consumption ◽  
Cross Sections ◽  
Computational Models ◽  
Minimum Energy ◽  
Thermal Capacity ◽  
Minimum Energy Consumption ◽  
Distributed Parameters ◽  
Analysis And Synthesis ◽  
Set Up ◽  
Object Of Control

The paper goes into peculiarities of using developed mathematical models of ceramsite burning and computational models created on their basis. The work is done in the light of analysis and synthesis of multivariate control system of kiln angular velocity and kiln charge with volumetric thermal capacity burners. It is shown that computational models of burning as an object of control are problem-oriented on producing ceramsite with specified density. Mathematical model of ceramsite burning process as an object with distributed parameters is synthesized. The transition from model with distributed parameters to the mode with lumped parameters is performed. Then the authors used a model with three bearing cross-sections along Z-axis in Matlab software and created a computational model of multivariate object of control with inter-channel connections. The paper presents experimental computational set up methods and methods of ceramsite burning optimal curve identification on the criterion of minimizing energy consumption for burning. The developed method of staging computational experiments makes it possible to predict the strength of ceramsite if values of control actions are known. The results of modeling help create methodology of choosing optimal modes of ceramsite burning with the required mark of strength and 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.

From Construction to Operation: Achieving Indoor Thermal Comfort via Altering External Walls Specifications in Egypt

2013 ◽  
Vol 689 ◽  
pp. 250-253 ◽  
Author(s):  
Mohamed M. Mahdy ◽  
Marialena Nikolopoulou
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Natural Ventilation ◽  
Minimum Energy ◽  
Residential Energy ◽  
Indoor Thermal Comfort ◽  
Minimum Energy Consumption ◽  
Different Types ◽  
Running Cost ◽  
The Cost

The objective of this research is to study the effect of using different material specifications for the external walls on the cost of the energy consumption for achieving internal thermal comfort. We refer to this as operation running cost, which in turn is compared to initial construction cost for each type of the used external walls. In order to achieve this objective, dynamic thermal simulation were carried out for four different types of external walls – commonly used in Egypt – in two different sets of cooling: natural ventilation and mechanical means. Experiments recommend that using the Egyptian Residential Energy Code (EREC) to achieve inner thermal comfort with the minimum energy consumption (consequently the minimum CO2 emissions) and the minimum running cost as well.

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