Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

2015 ◽  
pp. 446-477 ◽  
Author(s):  
Tianxing Cai
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Chemical Process ◽  
Chemical Engineering ◽  
Optimization Methods ◽  
Evolutionary Strategy ◽  
Artificial Intelligence Techniques ◽  
Bee Colony ◽  
Degree Of Satisfaction ◽  
Optimization And Control

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

2016 ◽  
pp. 1229-1260
Author(s):  
Tianxing Cai
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Chemical Process ◽  
Chemical Engineering ◽  
Optimization Methods ◽  
Evolutionary Strategy ◽  
Artificial Intelligence Techniques ◽  
Bee Colony ◽  
Degree Of Satisfaction ◽  
Optimization And Control

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

scholarly journals Improved Forms for Controlling the Acrobot with Motors of Atypical Size Using Artificial Intelligence Techniques

2017 ◽  
Author(s):  
Gonzalo Mier ◽  
Javier de Lope
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Control Problem ◽  
Inverted Pendulum ◽  
Artificial Intelligence Techniques ◽  
Rest Position ◽  
Planar Robot ◽  
Trajectory Generator

An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

scholarly journals Design of Air Cushion Vehicles Using Artificial Intelligence: Expert System and Genetic Algorithm

2021 ◽  
Author(s):  
Tae-Cheol Jung
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Expert System ◽  
Coast Guard ◽  
Ride Quality ◽  
Successful Implementation ◽  
Artificial Intelligence Techniques ◽  
Initial Design ◽  
Air Cushion ◽  
Thesis Work

In the thesis, initial design of an Air Cushion Vehicle (ACV)is performed with the expert system and its skirt system is further optimized with the genetic algorithm. Both the expert system and genetic algorithm are advanced computerized design techniques of artifical intelligence. Those techniques are specifically developed for the ACVs with programming codes in this thesis. Then the main objective is to show the successful implementation of those techniques in the design of ACVs. The thesis work is divided into two parts. In the first part, the general configuration of ACVs, including the overall dimensions, weight distribution, parametric properties, and several subsystems, is studied and designed by the expert system as an initial design phase. In the second part of the thesis, the skirt system of ACVs is further optimized. In particular, the properties of the bag and finger skirt are optimized for improved ride quality and stability by the genetic algorithm. For the validation of these two artificial intelligence techniques, the CCG (Canadian Coast Guard) 37 ton Waban-Aki and U.S. Navy's 150 ton LCAC (Landing Craft Air Cushion) are selected for the tests. The results of the tests proved that the expert system was successfully implemented and was a powerful tool for the initial design of ACVs. Furthermore, the genetic algorithm optimized the skirt system with significantly improved ride quality and stability. It was also shown that the skirt mass was an important design factor in the heave response of the bag and finger skirt. Hence, this thesis work opened the new possibility of designing ACVs with artificial intelligence techniques.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

scholarly journals Comparison of the characteristics of the genetic algorithm and the method of coordinates search for optimization of temperature modes indoor areas

2019 ◽  
pp. 260-270
Author(s):  
A P Shuravin ◽  
S V Vologdin
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Computational Experiment ◽  
Mathematical Formulation ◽  
Computational Cost ◽  
Optimization Methods ◽  
Search Method ◽  
Mathematical Apparatus ◽  
Temperature Mode ◽  
Project Smart

The article substantiates the relevance of optimization algorithms research for solving various applied problems and for the science of artificial intelligence. The need to solve problems of optimizing the thermal-hydraulic modes of buildings (as part of the project "Smart City") is explained. The paper presents a mathematical formulation of the problem of optimizing the temperature mode of rooms using adjustable devices. Existing work provides two methods for solving the posed problem. They are the coordinates search method and the genetic algorithm. The article contains the description of the above mentioned algorithms (including the mathematical apparatus used). The results of the computational experiment (for the considered optimization methods) are presented. These experimental results show that the genetic algorithm provides better optimization results than the coordinates search method, but it has a large computational cost. The hypothesis was confirmed that in order to increase the efficiency of solving the considered class of problems it is necessary to combine the genetic algorithm and the coordinates search method.

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