Multi-Objective Optimization of an Ethylene Oxide Reactor

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Allan Vandervoort ◽  
Jules Thibault ◽  
Yash P. Gupta
Keyword(s):  
Ethylene Oxide ◽  
Optimization Problem ◽  
Operating Conditions ◽  
Gradient Algorithm ◽  
Inlet Pressure ◽  
Multi Objective Optimization ◽  
Gas Temperature ◽  
Multi Objective ◽  
Pareto Domain ◽  
Input Variables

In this study, multi-objective optimization is performed for a reactor producing ethylene oxide from ethylene. The optimization considered three objectives: the maximization of the ethylene oxide production and selectivity, and the maximization of a safety factor related to the presence of oxygen in the reactor. The Pareto domain for this optimization problem was first approximated using the Objective-Based Gradient Algorithm, and the Pareto-optimal solutions were ranked using the Net-Flow procedure to determine the best operating conditions. From the optimization results, it is recommended that the ethylene oxide reactor be operated at high inlet pressure and gas temperature, and low inlet volumetric gas flowrate and chemical reaction moderator concentration. These operating conditions led to the highest ranked compromise solution, balancing the trade-off between each of the three objectives. Finally, it was found that a decrease in the inlet pressure or variation in the volumetric gas flowrate could readily lead to operating conditions outside of the Pareto domain, and these input variables should therefore be carefully controlled throughout operation of the reactor.

scholarly journals Wireless Sensor Network Optimization: Multi-Objective Paradigm

2021 ◽  
Author(s):  
Muhammad Iqbal ◽  
Muhammad Naeem ◽  
Alagan Anpalagan ◽  
Ashfaq Ahmed ◽  
Muhammad Azam
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Variables

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planning and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Utilizing Multiple Objectives for the Optimization of the Pultrusion Process Based on a Thermo-Chemical Simulation

2013 ◽  
Vol 554-557 ◽  
pp. 2165-2174 ◽  
Author(s):  
Cem C. Tutum ◽  
Ismet Baran ◽  
Jesper Hattel
Keyword(s):  
Optimization Problem ◽  
Operating Conditions ◽  
Die Design ◽  
Process Efficiency ◽  
Optimum Process ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pultrusion Process ◽  
Multi Objective

Pultrusion is one of the most effective manufacturing processes for producing composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the resin injection chamber, etc., to provide better understanding of the process, consequently to improve the efficiency of the process as well the product quality. Numerous simulation approaches have been presented until now. However, optimization studies had been limited with either experimental cases or determining only one objective to improve one aspect of the performance of the process. This objective is either augmented by other process related criteria or subjected to constraints which might have had the same importance of being treated as objectives. In essence, these approaches convert a true multi-objective optimization problem (MOP) into a single-objective optimization problem (SOP). This transformation obviously results in only one optimum solution and it does not support the efforts to get more out of an optimization study, such as relations between variables and objectives or constraints. In this study, an MOP considering thermo-chemical aspects of the pultrusion process (e.g. cure degree, temperatures), in which the pulling speed is maximized and the heating power is minimized simultaneously (without defining any preference between them), has been formulated. An evolutionary multi-objective optimization (EMO) algorithm, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), has been used to solve this MOP in an ideal way where the outcome is the set of multiple solutions (i.e. Pareto-optimal solutions) and each solution is theoretically an optimal solution corresponding to a particular trade-off among objectives. Following the solution process, in other words obtaining the Pareto-optimal front, a further postprocessing study has been performed to unveil some common principles existing between the variables, the objectives and the constraints either along the whole front or in some portion of it. These relationships will reveal a design philosophy not only for the improvement of the process efficiency, but also a methodology to design a pultrusion die for different operating conditions.

scholarly journals Wireless Sensor Network Optimization: Multi-Objective Paradigm

2021 ◽  
Author(s):  
Muhammad Iqbal ◽  
Muhammad Naeem ◽  
Alagan Anpalagan ◽  
Ashfaq Ahmed ◽  
Muhammad Azam
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Variables

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planning and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

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