A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the face of catalyst deactivation

2007 ◽  
Vol 46 (12) ◽  
pp. 1299-1309 ◽  
Author(s):  
H. Kordabadi ◽  
A. Jahanmiri
Keyword(s):  
Dynamic Optimization ◽  
Methanol Synthesis ◽  
Catalyst Deactivation ◽  
The Face ◽  
Dual Stage

scholarly journals Dynamic optimization of methanol synthesis section in the dual type configuration to increase methanol production

2019 ◽  
Vol 74 ◽  
pp. 90
Author(s):  
Samane Masoudi ◽  
Mohammad Farsi ◽  
Mohammad Reza Rahimpour
Keyword(s):  
Dynamic Optimization ◽  
Methanol Synthesis ◽  
Catalyst Deactivation ◽  
Dynamic Condition ◽  
Production Capacity ◽  
Operating Conditions ◽  
Second Step ◽  
Methanol Production ◽  
Equilibrium Conversion ◽  
Plant Data

The main object of this research is dynamic modeling and optimization of the methanol synthesis section in the dual type configuration considering catalyst deactivation to improve methanol production capacity. In the methanol unit, deactivation of CuO/ZnO/Al2O3 catalyst by sintering and low equilibrium conversion of reactions limit the production capacity, and changing operating temperature is a practical solution to overcome the production decay. In the first step, the considered process is modeled based on the mass and energy balance equations at dynamic condition. To prove the accuracy of developed model, the simulation results are compared with the plant data at the same operating conditions. In the second step, a dynamic optimization problem is formulated, and the optimal trajectories of manipulated variables are determined considering methanol production rate as the objective function. Finally, the performance of optimized process is compared with the conventional system at the same design conditions. The results show that operating at the optimal conditions increases methanol production capacity about 6.45%.

scholarly journals Automatic Approach for Mask Detection Effective: For COVID-19

2021 ◽  
Author(s):  
Debajyoty Banik ◽  
Saksham Rawat Rawat ◽  
Aayush Thakur ◽  
Pritee Parwekar ◽  
Suresh Chandra Satapathy
Keyword(s):  
Region Of Interest ◽  
Face Mask ◽  
Working Environment ◽  
Public Places ◽  
Detection Model ◽  
Face Region ◽  
Sense Of Security ◽  
Improper Use ◽  
The Face ◽  
Dual Stage

Abstract The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

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