1. This tutorial presents the design of a heat exchanger network for a crude pre-heat train. The network will heat crude oil using product streams before the oil enters a desalter and pre-flash unit.
2. Process and utility streams are created, including crude oil, product streams, cooling water, and boiler feed water. Heat exchangers are added to heat the crude using the product streams.
3. The worksheet is used to enter heat exchanger information and manipulate the network to complete the pre-flash section and overall heat exchanger network design.
The document discusses Aspen Plus and its physical property methods. It covers topics such as component specification, property methods, property sets, analysis tools, data regression, property estimation, and applications. Property methods include ideal, activity, equation of state, and special models. Parameters include pure component and binary interaction parameters. The document provides an overview of using Aspen Plus to model physical properties of pure components, binary mixtures, and more complex systems.
This document summarizes flooding in a distillation column. Distillation separates mixtures based on differences in volatility through boiling and vaporization. Flooding occurs when excessive vapor flow carries liquid up the column, reducing efficiency. It can be detected by increases in differential pressure and decreases in separation. The document describes an experiment where a distillation column's reboiler heat was incrementally increased. Measurements from pressure transmitters showed that filtering and monitoring standard deviation of the pressure signal could provide early detection of the column approaching flooding. This allows operators to make adjustments and prevent loss of separation and reduced efficiency.
This document provides copyright information and technical support contact details for Aspen Technology's HYSYS 2004.2 Dynamic Modeling software. It lists over 200 Aspen product names that are copyrighted and/or trademarked by Aspen Technology. Contact information is provided for Aspen's Online Technical Support Center, phone support, and email support.
Design and Rating of Packed Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 DESIGN PHILOSOPHY
5 PERFORMANCE GUARANTEES
6 DESCRIPTION OF PACKED COLUMN INTERNALS
7. DESIGN CALCULATIONS
7.1 Selection of Packing Size
7.2 Rough Design
7.3 Detailed Design and Rating
8 LIQUID DISTRIBUTION AND REDISTRIBUTION
8.1 Basic Concepts
8.2 Pour Point Density
8.3 Peripheral Irrigation - the Wall Zone
8.4 Distributor Levelness
8.5 Maximum Bed Height and Liquid Redistribution
9 PRACTICAL ASPECTS OF PACKED COLUMN DESIGN
9.1 Packing
9.2 Support Grid
9.3 Liquid Collector
9.4 Liquid Distributor or Redistributor
9.5 Packing Hold-down Grid
9.6 Reflux or Feed Pipe
9.7 Reboil Return Pipe
9.8 Liquid Draw-offs
9.9 Vapor Draw-offs
10 BIBLIOGRAPHY
APPENDICES
A DEFINITIONS
A.1 INTRODUCTION
A.2 MECHANICAL DEFINITIONS
A.3 PERFORMANCE DEFINITIONS
B PACKING HYDRAULICS - THE NORTON METHOD
TABLES
1 PACKING FACTORS FOR THE MORE COMMON
RANDOM PACKINGS
This is a slideshow / resource / support material of the course.
Get full access (videlectures)
https://www.chemicalengineeringguy.com/courses/aspen-plus-bootcamp-with-12-case-studies/
x-x-x
Requirements
Basic understanding of Plant Design & Operation
Strong Chemical Engineering Fundamentals
Aspen Plus V10 (at least 7.0)
Aspen Plus – Basic Process Modeling (Very Recommended)
Aspen Plus – Intermediate Process Modeling (Somewhat Recommended)
Description
This BOOTCAMP will show you how to model and simulate common industrial Chemical Processes.
It is focused on the “BOOTCAMP” idea, in which you will learn via workshops and case studies, minimizing theory to maximize learning.
You will learn about:
Better Flowsheet manipulation and techniques
Understand Property Method Selection and its effects on simulation results
More than 15 Unit Operations that can be used in any Industry
Model Analysis Tools required for process design
Reporting Relevant Results Plot relevant data
Analysis & Optimization of Chemical Plants
Economic Analysis
Dynamic Simulations
At the end of this Bootcamp, you will be able to model more industrial processes, feel confident when modeling new processes as well as applying what you have learnt to other industries.
1. This tutorial presents the design of a heat exchanger network for a crude pre-heat train. The network will heat crude oil using product streams before the oil enters a desalter and pre-flash unit.
2. Process and utility streams are created, including crude oil, product streams, cooling water, and boiler feed water. Heat exchangers are added to heat the crude using the product streams.
3. The worksheet is used to enter heat exchanger information and manipulate the network to complete the pre-flash section and overall heat exchanger network design.
The document discusses Aspen Plus and its physical property methods. It covers topics such as component specification, property methods, property sets, analysis tools, data regression, property estimation, and applications. Property methods include ideal, activity, equation of state, and special models. Parameters include pure component and binary interaction parameters. The document provides an overview of using Aspen Plus to model physical properties of pure components, binary mixtures, and more complex systems.
This document summarizes flooding in a distillation column. Distillation separates mixtures based on differences in volatility through boiling and vaporization. Flooding occurs when excessive vapor flow carries liquid up the column, reducing efficiency. It can be detected by increases in differential pressure and decreases in separation. The document describes an experiment where a distillation column's reboiler heat was incrementally increased. Measurements from pressure transmitters showed that filtering and monitoring standard deviation of the pressure signal could provide early detection of the column approaching flooding. This allows operators to make adjustments and prevent loss of separation and reduced efficiency.
This document provides copyright information and technical support contact details for Aspen Technology's HYSYS 2004.2 Dynamic Modeling software. It lists over 200 Aspen product names that are copyrighted and/or trademarked by Aspen Technology. Contact information is provided for Aspen's Online Technical Support Center, phone support, and email support.
Design and Rating of Packed Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 DESIGN PHILOSOPHY
5 PERFORMANCE GUARANTEES
6 DESCRIPTION OF PACKED COLUMN INTERNALS
7. DESIGN CALCULATIONS
7.1 Selection of Packing Size
7.2 Rough Design
7.3 Detailed Design and Rating
8 LIQUID DISTRIBUTION AND REDISTRIBUTION
8.1 Basic Concepts
8.2 Pour Point Density
8.3 Peripheral Irrigation - the Wall Zone
8.4 Distributor Levelness
8.5 Maximum Bed Height and Liquid Redistribution
9 PRACTICAL ASPECTS OF PACKED COLUMN DESIGN
9.1 Packing
9.2 Support Grid
9.3 Liquid Collector
9.4 Liquid Distributor or Redistributor
9.5 Packing Hold-down Grid
9.6 Reflux or Feed Pipe
9.7 Reboil Return Pipe
9.8 Liquid Draw-offs
9.9 Vapor Draw-offs
10 BIBLIOGRAPHY
APPENDICES
A DEFINITIONS
A.1 INTRODUCTION
A.2 MECHANICAL DEFINITIONS
A.3 PERFORMANCE DEFINITIONS
B PACKING HYDRAULICS - THE NORTON METHOD
TABLES
1 PACKING FACTORS FOR THE MORE COMMON
RANDOM PACKINGS
This is a slideshow / resource / support material of the course.
Get full access (videlectures)
https://www.chemicalengineeringguy.com/courses/aspen-plus-bootcamp-with-12-case-studies/
x-x-x
Requirements
Basic understanding of Plant Design & Operation
Strong Chemical Engineering Fundamentals
Aspen Plus V10 (at least 7.0)
Aspen Plus – Basic Process Modeling (Very Recommended)
Aspen Plus – Intermediate Process Modeling (Somewhat Recommended)
Description
This BOOTCAMP will show you how to model and simulate common industrial Chemical Processes.
It is focused on the “BOOTCAMP” idea, in which you will learn via workshops and case studies, minimizing theory to maximize learning.
You will learn about:
Better Flowsheet manipulation and techniques
Understand Property Method Selection and its effects on simulation results
More than 15 Unit Operations that can be used in any Industry
Model Analysis Tools required for process design
Reporting Relevant Results Plot relevant data
Analysis & Optimization of Chemical Plants
Economic Analysis
Dynamic Simulations
At the end of this Bootcamp, you will be able to model more industrial processes, feel confident when modeling new processes as well as applying what you have learnt to other industries.
Engineers often use softwares to perform gas compressor calculations to estimate compressor duty, temperatures, adiabatic & polytropic efficiencies, driver & cooler duty. In the following exercise, gas compressor calculations for a pipeline composition are shown as an example case study.
This document provides an overview of gas absorption and gas-liquid system design. It discusses general design procedures and considerations for gas-absorption systems using packed or plate towers. These include selecting solvents and solubility data, calculating liquid-to-gas ratios, determining packed height and transfer units, and accounting for heat effects. It also covers multicomponent systems, absorption with chemical reactions, gas-liquid contacting equipment like packed and plate columns, and design of these systems. Parameters discussed include pressure drop, flooding, loading, liquid distribution, interfacial area and mass transfer effectiveness.
Valvula masoneilan handbook for control valve sizingJupira Silva
This document provides an overview of formulas and concepts for sizing control valves, including:
- Definitions of key terms like flow coefficient (Cv) and pressure recovery factor.
- Equations for calculating liquid and gas flow rates through valves.
- Factors that affect valve sizing like pressure drop, specific gravity, compressibility.
- Concepts like cavitation that must be considered to avoid valve damage.
- How to account for factors like pipe reducers that influence valve performance.
- Tables of constants to use with the provided equations depending on the system units.
The document is a reference for engineers, providing formulas, explanations of concepts, and considerations necessary to properly size
This document provides an introduction to steady-state design and operation of distillation columns. It discusses key concepts in distillation including:
- Equilibrium stage concept for modeling both tray and packed columns
- Vapor-liquid equilibrium and the importance of relative volatility
- Minimum number of stages and minimum energy requirements for ideal binary separations using concepts like Fenske's equation
- Methods for estimating relative volatility and designing columns to meet purity specifications
- Typical profiles for composition, temperature, and other variables along the height of distillation columns
This document discusses the aMDEA process for removing carbon dioxide from process gas. It begins with an introduction and table of contents, then covers the need for CO2 removal, desirable solvent properties, commercially available processes, differences between physical and chemical absorption, selection criteria for processes, and an overview of the aMDEA process including constituents and reactions. It also discusses why the Rectisol process is not suitable, favorable absorption and regeneration parameters, common problems encountered, handling precautions, process interlocks, and problems and mistakes to avoid.
The document provides an overview of a module on flare system design and calculation. It discusses gas flaring definitions, components of a flare system, types of flares, environmental impacts, and considerations for flare system design and sizing calculations. Key aspects covered include gas flaring principles, when flaring occurs, composition of flared gases, reducing flaring through recovery systems, and sizing the flare header to minimize backpressure while limiting gas velocity.
Fired Equipment presentation on Types, Classification and governing Equations...Hassan ElBanhawi
Based on my 8 years of experience in Oil & Gas industry I can claim that you can find here All what you need to know about Fired Equipment. This is an introduction to understand more about their:-
-Types
-Basic Principles and equations
-Worked Example
You can find also more at:
http://hassanelbanhawi.com/staticequipment/firedequipment/
All the data and the illustrative figures presented here can be found through two reference books:-
ENGINEERING DATA BOOK by Gas Processors Suppliers Association
Process Technology - Equipment and Systems by Charles E. Thomas
Thank you.
This document provides an overview of the mid and downstream business training module. It discusses the typical design cycle for a plant from feasibility study through to commissioning. It also describes the various stages of engineering including FEED, review, and stage 0. Examples are provided of key deliverables for stage 0 such as updated P&IDs, process datasheets, equipment lists, utility summaries, and more. The purpose is to familiarize trainees with the engineering design process and key documents/outputs for midstream and downstream oil and gas facilities.
- The document discusses sizing pressure safety valves (PSVs) for oil and gas facilities.
- It covers PSV types, causes of chattering, and outlines the step-by-step process for sizing calculations including developing relief scenarios, determining required relief areas, and selecting valve sizes.
- Relief scenarios considered include blocked outlets, thermal expansion, tube rupture, gas blow-by, inlet valve failure, and exterior fires. Relief calculations involve assessing single-phase, two-phase, and transient relief situations.
Packed columns are used for distillation, gas absorption, and liquid-liquid extraction. They have continuous gas-liquid contact through a packed bed, unlike plate columns which have stage-wise contact. Packed columns depend on good liquid and gas distribution, and have lower holdup but higher pressure drop than plate columns. This document provides details on packed column components, design procedures such as selecting packing and determining height, and examples of absorption and stripping processes in packed columns.
This document provides an overview of using HYSYS simulation software to model and analyze chemical processes. It discusses setting up a HYSYS case by adding components, selecting a fluid package, and entering the simulation environment. It also covers defining process units like separators and heat exchangers, specifying stream properties, performing flash calculations, and generating workbooks. The document is intended as an introduction for students to learn the basic functionality of HYSYS through examples of common unit operations.
This document discusses methods for estimating capital and product costs. It describes several methods for estimating capital costs, including: (1) a detailed-item estimate, (2) a unit cost estimate based on existing data, and (3) estimating other capital costs as percentages of the delivered equipment cost. It also covers the power factor method of scaling costs based on capacity, as well as estimating capital using the turnover ratio. The document provides an overview of cost components and recommends methods suited to different levels of estimate accuracy needed.
This document provides a conceptual plant design for producing styrene through the dehydrogenation of ethylbenzene. Key details include:
- The plant will have a capacity of 100 million kg per year of styrene.
- A single packed-bed reactor operating at 600°C and 2 atm using a proprietary iron catalyst will achieve 56% conversion of ethylbenzene with 81% selectivity to styrene.
- Three distillation columns will separate and purify the reactor effluent streams into 99.7% toluene, 99.5% benzene, and 99.8% styrene product streams.
- Economic analysis estimates a total capital investment of $37.4 million
The document provides data on steam flows, pressures, and temperatures at the inlet, extraction, and condensing sections of a turbine. It then calculates the efficiencies of the extraction and condensing sections. For the extraction section, it calculates the inlet steam enthalpy, extraction steam enthalpy and entropy, and isentropic extraction steam enthalpy. Using these values, it determines the extraction section efficiency is 67%. For the condensing section, it states efficiency will be calculated but does not show the calculation.
Control of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 GENERAL DESCRIPTION OF A DISTILLATION COLUMN
5 REGULATORY CONTROL
5.1 Composition Control
5.2 Mass Balance Control
5.3 Design of Feedback Control Systems
5.4 Pressure and Condensation Control
5.5 Reboiler Control
6 DISTURBANCE COMPENSATION
6.1 Feed-forward Control
6.2 Cascade Control
6.3 Internal Reflux Control
7 CONSTRAINT CONTROL
7.1 Override Controls
7.2 Flooding
7.3 Limiting Control
8 MORE ADVANCED TOPICS
8.1 Temperature Position Control
8.2 Inferential Measurement
8.1 Floating Pressure Control
8.2 Model Based Predictive Control
8.1 Control of Side-streams
8.2 Extractive/Azeotropic Systems
9 REFERENCES
TABLES
1 SYMPTOMS OF IMBALANCE AND THE REGULATORY VARIABLES
2 PRACTICAL LINKAGES BETWEEN CONTROL
(P, R, B, C) AND REGULATION VARIABLES
(h, r, d, b, c, v)
3 COMPOSITION REGULATION
4 COMPOSITION REGULATION - VERY SMALL FLOWS
Design of Methanol Water Distillation Column Rita EL Khoury
Methanol is an essential feed stock for the manufacture of many industrial products such as adhesives and paints and it is widely used as a solvent in many chemical reactions. Crude methanol is obtained from steam reforming of natural gas and then a purification process is needed since it contains smaller and larger degree of impurities.
The purification process consists of two steps: a topping column used to remove the low boiling impurity called the light ends; and the remaining water methanol mixture is transferred to another column called the refining column where it is constantly boiled until separation occurs. Methanol rises to the top while the water accumulates in the bottom.
This document focuses on methanol water separation. A detailed design study for the distillation column is conducted where the separation occurs at atmospheric pressure with a total condenser and a partial reboiler.
Steam is a widely used and efficient energy carrier that can be generated and distributed cost effectively via pipe networks. It can transfer large amounts of heat energy for applications like mechanical power, heating buildings and industrial processes. Modern steam systems are automated and easy to control precisely via pressure and temperature regulation. Steam plants are durable, flexible for multiple uses, and when well maintained can operate efficiently for many years.
This document provides guidelines for preliminary sizing of liquid and gas piping systems. It discusses parameters to consider like velocity, pressure drop, Reynolds number and friction factor. For liquid piping, it recommends velocity ranges of 0.5-5 m/s and pressure drops of 0.034-0.083 bar/100m for pump suction and 0.15-0.627 bar/100m for pump discharge. It also lists velocity ranges for different gas and steam systems and formulas to calculate Reynolds number, friction factor and pressure drop based on parameters like density, velocity, pipe diameter and length.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Engineers often use softwares to perform gas compressor calculations to estimate compressor duty, temperatures, adiabatic & polytropic efficiencies, driver & cooler duty. In the following exercise, gas compressor calculations for a pipeline composition are shown as an example case study.
This document provides an overview of gas absorption and gas-liquid system design. It discusses general design procedures and considerations for gas-absorption systems using packed or plate towers. These include selecting solvents and solubility data, calculating liquid-to-gas ratios, determining packed height and transfer units, and accounting for heat effects. It also covers multicomponent systems, absorption with chemical reactions, gas-liquid contacting equipment like packed and plate columns, and design of these systems. Parameters discussed include pressure drop, flooding, loading, liquid distribution, interfacial area and mass transfer effectiveness.
Valvula masoneilan handbook for control valve sizingJupira Silva
This document provides an overview of formulas and concepts for sizing control valves, including:
- Definitions of key terms like flow coefficient (Cv) and pressure recovery factor.
- Equations for calculating liquid and gas flow rates through valves.
- Factors that affect valve sizing like pressure drop, specific gravity, compressibility.
- Concepts like cavitation that must be considered to avoid valve damage.
- How to account for factors like pipe reducers that influence valve performance.
- Tables of constants to use with the provided equations depending on the system units.
The document is a reference for engineers, providing formulas, explanations of concepts, and considerations necessary to properly size
This document provides an introduction to steady-state design and operation of distillation columns. It discusses key concepts in distillation including:
- Equilibrium stage concept for modeling both tray and packed columns
- Vapor-liquid equilibrium and the importance of relative volatility
- Minimum number of stages and minimum energy requirements for ideal binary separations using concepts like Fenske's equation
- Methods for estimating relative volatility and designing columns to meet purity specifications
- Typical profiles for composition, temperature, and other variables along the height of distillation columns
This document discusses the aMDEA process for removing carbon dioxide from process gas. It begins with an introduction and table of contents, then covers the need for CO2 removal, desirable solvent properties, commercially available processes, differences between physical and chemical absorption, selection criteria for processes, and an overview of the aMDEA process including constituents and reactions. It also discusses why the Rectisol process is not suitable, favorable absorption and regeneration parameters, common problems encountered, handling precautions, process interlocks, and problems and mistakes to avoid.
The document provides an overview of a module on flare system design and calculation. It discusses gas flaring definitions, components of a flare system, types of flares, environmental impacts, and considerations for flare system design and sizing calculations. Key aspects covered include gas flaring principles, when flaring occurs, composition of flared gases, reducing flaring through recovery systems, and sizing the flare header to minimize backpressure while limiting gas velocity.
Fired Equipment presentation on Types, Classification and governing Equations...Hassan ElBanhawi
Based on my 8 years of experience in Oil & Gas industry I can claim that you can find here All what you need to know about Fired Equipment. This is an introduction to understand more about their:-
-Types
-Basic Principles and equations
-Worked Example
You can find also more at:
http://hassanelbanhawi.com/staticequipment/firedequipment/
All the data and the illustrative figures presented here can be found through two reference books:-
ENGINEERING DATA BOOK by Gas Processors Suppliers Association
Process Technology - Equipment and Systems by Charles E. Thomas
Thank you.
This document provides an overview of the mid and downstream business training module. It discusses the typical design cycle for a plant from feasibility study through to commissioning. It also describes the various stages of engineering including FEED, review, and stage 0. Examples are provided of key deliverables for stage 0 such as updated P&IDs, process datasheets, equipment lists, utility summaries, and more. The purpose is to familiarize trainees with the engineering design process and key documents/outputs for midstream and downstream oil and gas facilities.
- The document discusses sizing pressure safety valves (PSVs) for oil and gas facilities.
- It covers PSV types, causes of chattering, and outlines the step-by-step process for sizing calculations including developing relief scenarios, determining required relief areas, and selecting valve sizes.
- Relief scenarios considered include blocked outlets, thermal expansion, tube rupture, gas blow-by, inlet valve failure, and exterior fires. Relief calculations involve assessing single-phase, two-phase, and transient relief situations.
Packed columns are used for distillation, gas absorption, and liquid-liquid extraction. They have continuous gas-liquid contact through a packed bed, unlike plate columns which have stage-wise contact. Packed columns depend on good liquid and gas distribution, and have lower holdup but higher pressure drop than plate columns. This document provides details on packed column components, design procedures such as selecting packing and determining height, and examples of absorption and stripping processes in packed columns.
This document provides an overview of using HYSYS simulation software to model and analyze chemical processes. It discusses setting up a HYSYS case by adding components, selecting a fluid package, and entering the simulation environment. It also covers defining process units like separators and heat exchangers, specifying stream properties, performing flash calculations, and generating workbooks. The document is intended as an introduction for students to learn the basic functionality of HYSYS through examples of common unit operations.
This document discusses methods for estimating capital and product costs. It describes several methods for estimating capital costs, including: (1) a detailed-item estimate, (2) a unit cost estimate based on existing data, and (3) estimating other capital costs as percentages of the delivered equipment cost. It also covers the power factor method of scaling costs based on capacity, as well as estimating capital using the turnover ratio. The document provides an overview of cost components and recommends methods suited to different levels of estimate accuracy needed.
This document provides a conceptual plant design for producing styrene through the dehydrogenation of ethylbenzene. Key details include:
- The plant will have a capacity of 100 million kg per year of styrene.
- A single packed-bed reactor operating at 600°C and 2 atm using a proprietary iron catalyst will achieve 56% conversion of ethylbenzene with 81% selectivity to styrene.
- Three distillation columns will separate and purify the reactor effluent streams into 99.7% toluene, 99.5% benzene, and 99.8% styrene product streams.
- Economic analysis estimates a total capital investment of $37.4 million
The document provides data on steam flows, pressures, and temperatures at the inlet, extraction, and condensing sections of a turbine. It then calculates the efficiencies of the extraction and condensing sections. For the extraction section, it calculates the inlet steam enthalpy, extraction steam enthalpy and entropy, and isentropic extraction steam enthalpy. Using these values, it determines the extraction section efficiency is 67%. For the condensing section, it states efficiency will be calculated but does not show the calculation.
Control of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 GENERAL DESCRIPTION OF A DISTILLATION COLUMN
5 REGULATORY CONTROL
5.1 Composition Control
5.2 Mass Balance Control
5.3 Design of Feedback Control Systems
5.4 Pressure and Condensation Control
5.5 Reboiler Control
6 DISTURBANCE COMPENSATION
6.1 Feed-forward Control
6.2 Cascade Control
6.3 Internal Reflux Control
7 CONSTRAINT CONTROL
7.1 Override Controls
7.2 Flooding
7.3 Limiting Control
8 MORE ADVANCED TOPICS
8.1 Temperature Position Control
8.2 Inferential Measurement
8.1 Floating Pressure Control
8.2 Model Based Predictive Control
8.1 Control of Side-streams
8.2 Extractive/Azeotropic Systems
9 REFERENCES
TABLES
1 SYMPTOMS OF IMBALANCE AND THE REGULATORY VARIABLES
2 PRACTICAL LINKAGES BETWEEN CONTROL
(P, R, B, C) AND REGULATION VARIABLES
(h, r, d, b, c, v)
3 COMPOSITION REGULATION
4 COMPOSITION REGULATION - VERY SMALL FLOWS
Design of Methanol Water Distillation Column Rita EL Khoury
Methanol is an essential feed stock for the manufacture of many industrial products such as adhesives and paints and it is widely used as a solvent in many chemical reactions. Crude methanol is obtained from steam reforming of natural gas and then a purification process is needed since it contains smaller and larger degree of impurities.
The purification process consists of two steps: a topping column used to remove the low boiling impurity called the light ends; and the remaining water methanol mixture is transferred to another column called the refining column where it is constantly boiled until separation occurs. Methanol rises to the top while the water accumulates in the bottom.
This document focuses on methanol water separation. A detailed design study for the distillation column is conducted where the separation occurs at atmospheric pressure with a total condenser and a partial reboiler.
Steam is a widely used and efficient energy carrier that can be generated and distributed cost effectively via pipe networks. It can transfer large amounts of heat energy for applications like mechanical power, heating buildings and industrial processes. Modern steam systems are automated and easy to control precisely via pressure and temperature regulation. Steam plants are durable, flexible for multiple uses, and when well maintained can operate efficiently for many years.
This document provides guidelines for preliminary sizing of liquid and gas piping systems. It discusses parameters to consider like velocity, pressure drop, Reynolds number and friction factor. For liquid piping, it recommends velocity ranges of 0.5-5 m/s and pressure drops of 0.034-0.083 bar/100m for pump suction and 0.15-0.627 bar/100m for pump discharge. It also lists velocity ranges for different gas and steam systems and formulas to calculate Reynolds number, friction factor and pressure drop based on parameters like density, velocity, pipe diameter and length.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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