Profit Maximization Techniques for Operating Chemical Plants

Profit Maximization Techniques for Operating Chemical Plants
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A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins. The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvements—there is a plethora of information to help keep plant operations on budget. The book also includes information on: ● Take away methods and techniques for identifying and exploiting potential areas to improve profit within the plant ● Focus on latest Artificial Intelligence based modeling, knowledge discovery and optimization strategies to maximize profit in running plant. ● Describes procedure to develop advance process monitoring and fault diagnosis in running plant ● Thoughts on engineering design , best practices and monitoring to sustain profit improvements ● Step-by-step guides to identifying, building, and deploying improvement applications For leaders and technologists in the industry who want to maximize profit margins, this text provides basic concepts, guidelines, and step-by-step guides specifically for the chemical plant sector.

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Sandip K. Lahiri. Profit Maximization Techniques for Operating Chemical Plants

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Profit Maximization Techniques for Operating Chemical Plants

Figure List

Table List

Preface

Overview of Contents

Concept of Profit Maximization

Big Picture of the Modern Chemical Industry

Profit Maximization Project (PMP) Implementation Steps

Strategy of Profit Maximization

Key Performance Indicators and Targets

Assessment of Current Plant Status

Process Modeling by an Artificial Neural Network

Optimization of Industrial Processes and Process Equipment

Process Monitoring

Fault Diagnosis

Optimization of the Existing Distillation Column

New Design Methodology

Genetic Programing for Modeling of Industrial Reactors

Maximum Capacity Test Run and Debottlenecking Study

Loss Assessment

Advance Process Control

150 Ways and Best Practices to Improve Profit in Running a Chemical Plant

1 Concept of Profit Maximization. 1.1 Introduction

1.2 Who is This Book Written for?

1.3 What is Profit Maximization and Sweating of Assets All About?

1.4 Need for Profit Maximization in Today's Competitive Market

1.5 Data Rich but Information Poor Status of Today's Process Industries

1.6 Emergence of Knowledge‐Based Industries

1.7 How Knowledge and Data Can Be Used to Maximize Profit

References

2 Big Picture of the Modern Chemical Industry. 2.1 New Era of the Chemical Industry

2.2 Transition from a Conventional to an Intelligent Chemical Industry

2.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected?

2.3.1 Attaining a New Level of Functional Excellence

2.3.1.1 Manufacturing

2.3.1.2 Supply Chain

2.3.1.3 Sales and Marketing

2.3.1.4 Research and Development

2.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing

2.4.1 Decreasing Downtime Through Analytics

2.4.2 Increase Profits with Less Resources

2.4.3 Optimizing the Whole Production Process

2.5 Achieving Business Impact with Data

2.5.1 Data's Exponential Growing Importance in Value Creation

2.5.2 Different Links in the Value Chain

2.5.2.1 The Insights Value Chain – Definitions and Considerations (Holger Hürtgen, 2018)

2.6 From Dull Data to Critical Business Insights: The Upstream Processes

2.6.1 Generating and Collecting Relevant Data

2.6.2 Data Refinement is a Two‐Step Iteration

2.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities

2.7.1 Turning Insights into Action

2.7.2 Developing Data Culture

2.7.3 Mastering Tasks Concerning Technology and Infrastructure as Well as Organization and Governance

References

3 Profit Maximization Project (PMP) Implementation Steps. 3.1 Implementing a Profit Maximization Project (PMP)

3.1.1 Step 1: Mapping the Whole Plant in Monetary Terms

3.1.2 Step 2: Assessment of Current Plant Conditions

3.1.3 Step 3: Assessment of the Base Control Layer of the Plant

3.1.4 Step 4: Assessment of Loss from the Plant

3.1.5 Step 5: Identification of Improvement Opportunity in Plant and Functional Design of PMP Applications

3.1.6 Step 6: Develop an Advance Process Monitoring Framework by Applying the Latest Data Analytics Tools

3.1.7 Step 7: Develop a Real‐Time Fault Diagnosis System

3.1.8 Step 8: Perform a Maximum Capacity Test Run

3.1.9 Step 9: Develop and Implement Real‐Time APC

3.1.10 Step 10: Develop a Data‐Driven Offline Process Model for Critical Process Equipment

3.1.11 Step 11: Optimizing Process Operation with a Developed Model

3.1.12 Step 12: Modeling and Optimization of Industrial Reactors

3.1.13 Step 13: Maximize Throughput of All Running Distillation Columns

3.1.14 Step 14: Apply New Design Methodology for Process Equipment

References

4 Strategy for Profit Maximization. 4.1 Introduction

4.2 How is Operating Profit Defined in CPI?

4.3 Different Ways to Maximize Operating Profit

4.4 Process Cost Intensity. 4.4.1 Definition of Process Cost Intensity

4.4.2 Concept of Cost Equivalent (CE)

4.4.3 Cost Intensity for a Total Site

4.5 Mapping the Whole Process in Monetary Terms and Gain Insights

4.6 Case Study of a Glycol Plant

4.7 Steps to Map the Whole Plant in Monetary Terms and Gain Insights

4.7.1 Step 1: Visualize the Plant as a Black Box

4.7.2 Step 2: Data Collection from a Data Historian and Preparation of Cost Data

4.7.3 Step 3: Calculation of Profit Margin

4.7.4 Step 4: Gain Insights from Plant Cost and Profit Data

4.7.5 Step 5: Generation of Production Cost and a Profit Margin Table for One Full Year

4.7.6 Step 6: Plot Production Cost and Profit Margin for One Full Year and Gain Insights

4.7.7 Step 7: Calculation of Relative Standard Deviations of each Parameter in order to Understand the Cause of Variability

4.7.8 Step 8: Cost Benchmarking

Reference

5 Key Performance Indicators and Targets

5.1 Introduction

5.2 Key Indicators Represent Operation Opportunities

5.2.1 Reaction Optimization

5.2.2 Heat Exchanger Operation Optimization

5.2.3 Furnace Operation

5.2.4 Rotating Equipment Operation

5.2.5 Minimizing Steam Letdown Flows

5.2.6 Turndown Operation

5.2.7 Housekeeping Aspects

5.3 Define Key Indicators

5.3.1 Process Analysis and Economics Analysis

5.3.2 Understand the Constraints

5.3.3 Identify Qualitatively Potential Area of Opportunities

5.4 Case Study of Ethylene Glycol Plant to Identify the Key Performance Indicator. 5.4.1 Methodology

5.4.2 Ethylene Oxide Reaction Section. 5.4.2.1 Understand the Process

5.4.2.2 Understanding the Economics of the Process

5.4.2.3 Factors that can Change the Production Cost and Overall Profit Generated from this Section

5.4.2.4 How is Production Cost Related to Process Parameters from the Standpoint of the Cause and Effect Relationship?

5.4.2.5 Constraints

5.4.2.6 Key Parameter Identifications

5.4.3 Cycle Water System. 5.4.3.1 Main Purpose

5.4.3.2 Economics of the Process

5.4.3.3 Factors that can Change the Production Cost of this Section

5.4.3.4 Constraints

5.4.3.5 Key Performance Parameters

5.4.4 Carbon Dioxide Removal Section. 5.4.4.1 Main Purpose

5.4.4.2 Economics

5.4.4.3 Factors that can Change the Production Cost of this Section

5.4.4.4 Constraints

5.4.4.5 Key Performance Parameters

5.4.5 EG Reaction and Evaporation Section. 5.4.5.1 Main Purpose

5.4.5.2 Economics

5.4.5.3 Factors that can Change the Production Cost of this Section

5.4.5.4 Key Performance Parameters

5.4.6 EG Purification Section. 5.4.6.1 Main Purpose

5.4.6.2 Economics

5.4.6.3 Key Performance Parameters

5.5 Purpose to Develop Key Indicators

5.6 Set up Targets for Key Indicators

5.7 Cost and Profit Dashboard. 5.7.1 Development of Cost and Profit Dashboard to Monitor the Process Performance in Money Terms

5.7.2 Connecting Key Performance Indicators in APC

5.8 It is Crucial to Change the Viewpoints in Terms of Cost or Profit

References

6 Assessment of Current Plant Status

6.1 Introduction

6.1.1 Data Extraction from a Data Historian

6.1.2 Calculate the Economic Performance of the Section

6.2 Monitoring Variations of Economic Process Parameters

6.3 Determination of the Effect of Atmosphere on the Plant Profitability

6.4 Capacity Variations

6.5 Assessment of Plant Reliability

6.6 Assessment of Profit Suckers and Identification of Equipment for Modeling and Optimization

6.7 Assessment of Process Parameters Having a High Impact on Profit

6.8 Comparison of Current Plant Performance Against Its Design

6.9 Assessment of Regulatory Control System Performance

6.9.1 Basic Assessment Procedure

6.10 Assessment of Advance Process Control System Performance

6.11 Assessment of Various Profit Improvement Opportunities

References

7 Process Modeling by the Artificial Neural Network. 7.1 Introduction

7.2 Problems to Develop a Phenomenological Model for Industrial Processes

7.3 Types of Process Model

7.3.1 First Principle‐Based Model

7.3.2 Data‐Driven Models

7.3.3 Grey Model

7.3.4 Hybrid Model

7.4 Emergence of Artificial Neural Networks as One of the Promising Data‐Driven Modeling Techniques

7.5 ANN‐Based Modeling. 7.5.1 How Does ANN Work?

7.5.2 Network Architecture

7.5.3 Back‐Propagation Algorithm (BPA)

7.5.4 Training

7.5.5 Generalizability

7.6 Model Development Methodology

7.6.1 Data Collection and Data Inspection

7.6.2 Data Pre‐processing and Data Conditioning (Lahiri, 2017)

7.6.2.1 Outlier Detection and Replacement (Lahiri, 2017)

7.6.2.2 Univariate Approach to Detect Outliers

7.6.2.3 Multivariate Approach to Detect Outliers (Lin, 2007)

7.6.3 Selection of Relevant Input–Output Variables

7.6.4 Align Data

7.6.5 Model Parameter Selection, Training, and Validation (Kadlec, Gabrys, & Strandt, 2009; Lin, 2007)

7.6.6 Model Acceptance and Model Tuning

7.7 Application of ANN Modeling Techniques in the Chemical Process Industry

7.8 Case Study: Application of the ANN Modeling Technique to Develop an Industrial Ethylene Oxide Reactor Model. 7.8.1 Origin of the Present Case Study

7.8.2 Problem Definition of the Present Case Study

7.8.3 Developing the ANN‐Based Reactor Model (Lahiri & Khalfe, 2008, 2009b, 2010)

7.8.4 Identifying Input and Output Parameters

7.8.5 Data Collection

7.8.6 Neural Regression

7.8.7 Results and Discussions

7.9 Matlab Code to Generate the Best ANN Model

References

Appendix 7.1 Matlab Code to Generate the Best ANN Model

8 Optimization of Industrial Processes and Process Equipment. 8.1 Meaning of Optimization in an Industrial Context

8.2 How Can Optimization Increase Profit?

8.3 Types of Optimization

8.3.1 Steady‐State Optimization

8.3.2 Dynamic Optimization

8.4 Different Methods of Optimization. 8.4.1 Classical Method

8.4.2 Gradient‐Based Methods of Optimization

8.4.3 Non‐traditional Optimization Techniques

8.5 Brief Historical Perspective of Heuristic‐based Non‐traditional Optimization Techniques

8.6 Genetic Algorithm. 8.6.1 What is Genetic Algorithm?

8.6.2 Foundation of Genetic Algorithms

8.6.3 Five Phases of Genetic Algorithms

8.6.3.1 Initial Population

8.6.3.2 Fitness Function

8.6.3.3 Selection

8.6.3.4 Crossover

8.6.3.5 Termination

8.6.4 The Problem Definition

8.6.5 Calculation Steps of GA (Babu, 2004)

8.6.5.1 Step 1: Generating Initial Population by Creating Binary Coding

8.6.5.2 Step 2: Evaluation of Fitness

8.6.5.3 Step 3: Selecting the Next Generation's Population

8.6.6 Advantages of GA Against Classical Optimization Techniques

8.7 Differential Evolution. 8.7.1 What is Differential Evolution (DE)?

8.7.2 Working Principle of DE (Babu, 2004)

8.7.3 Calculation Steps Performed in DE

8.7.4 Choice of DE Key Parameters (NP, F, and CR)

8.7.5 Stepwise Calculation Procedure for DE implementation (Babu, 2004)

8.8 Simulated Annealing. 8.8.1 What is Simulated Annealing?

8.8.2 Procedure (Babu, 2004)

8.8.3 Algorithm

8.9 Case Study: Application of the Genetic Algorithm Technique to Optimize the Industrial Ethylene Oxide Reactor

8.9.1 Conclusion of the Case Study

8.10 Strategy to Utilize Data‐Driven Modeling and Optimization Techniques to Solve Various Industrial Problems and Increase Profit

References

Appendix 8.1 Matlab Code for GA Optimization of an EO Reactor Case Study

9 Process Monitoring. 9.1 Need for Advance Process Monitoring

9.2 Current Approaches to Process Monitoring and Diagnosis

9.3 Development of an Online Intelligent Monitoring System

9.4 Development of KPI‐Based Process Monitoring

9.5 Development of a Cause and Effect‐Based Monitoring System

9.6 Development of Potential Opportunity‐Based Dash Board

9.6.1 Development of Loss and Waste Monitoring Systems

9.6.2 Development of a Cost‐Based Monitoring System

9.6.3 Development of a Constraints‐Based Monitoring System

9.7 Development of Business Intelligent Dashboards

9.8 Development of Process Monitoring System Based on Principal Component Analysis

9.8.1 What is a Principal Component Analysis?

9.8.2 Why Do We Need to Rotate the Data?

9.8.3 How Do We Generate Principal Components?

9.8.4 Steps to Calculating the Principal Components

9.9 Case Study for Operational State Identification and Monitoring Using PCA

9.9.1 Case Study 1: Monitoring a Reciprocating Reclaim Compressor

References

10 Fault Diagnosis. 10.1 Challenges to the Chemical Industry

10.2 What is Fault Diagnosis?

10.3 Benefit of a Fault Diagnosis System

10.3.1 Characteristic of an Automated Fault Diagnosis System

10.4 Decreasing Downtime Through a Fault Diagnosis Type Data Analytics

10.5 User Perspective to Make an Effective Fault Diagnosis System

10.6 How Are Fault Diagnosis Systems Made?

10.6.1 Principal Component‐Based Approach

10.6.2 Artificial Neural Network‐Based Approach

10.7 A Case Study to Build a Robust Fault Diagnosis System

10.7.1 Challenges to a Build Fault Diagnosis of an Ethylene Oxide Reactor System

10.7.2 PCA‐Based Fault Diagnosis of an EO Reactor System

10.7.3 Acquiring Historic Process Data Sets to Build a PCA Model

10.7.4 Criteria of Selection of Input Parameters for PCA

10.7.5 How PCA Input Data is Captured in Real Time

10.7.6 Building the Model

10.7.6.1 Calculations of the Principal Components

10.7.6.2 Calculations of Hotelling's T2

10.7.6.3 Calculations of the Residual

10.7.7 Creation of a PCA Plot for Training Data

10.7.8 Creation of Hotelling's T2 Plot for the Training Data

10.7.9 Creation of a Residual Plot for the Training Data

10.7.10 Creation of an Abnormal Zone in the PCA Plot

10.7.11 Implementing the PCA Model in Real Time

10.7.12 Detecting Whether the Plant is Running Normally or Abnormally on a Real ‐Time Basis

10.7.13 Use of a PCA Plot During Corrective Action in Real Time

10.7.14 Validity of a PCA Model. 10.7.14.1 Time‐Varying Characteristic of an EO Catalyst

10.7.14.2 Capturing the Efficiency of the PCA Model Using the Residual Plot

10.7.15 Quantitive Decision Criteria Implemented for Retraining of an Ethylene Oxide (EO) Reactor PCA Model

10.7.16 How Retraining is Practically Executed

10.8 Building an ANN Model for Fault Diagnosis of an EO Reactor. 10.8.1 Acquiring Historic Process Data Sets to Build an ANN Model

10.8.2 Identification of Input and Output Parameters

10.8.3 Building of an ANN‐Based EO Reactor Model. 10.8.3.1 Complexity of EO Reactor Modeling

10.8.3.2 Model Building

10.8.4 Prediction Performance of an ANN Model

10.8.5 Utilization of an ANN Model for Fault Detection

10.8.6 How Do PCA Input Data Relate to ANN Input/Output Data?

10.8.7 Retraining of an ANN Model

10.9 Integrated Robust Fault Diagnosis System

10.10 Advantages of a Fault Diagnosis System

References

11 Optimization of an Existing Distillation Column. 11.1 Strategy to Optimize the Running Distillation Column

11.1.1 Strategy

11.2 Increase the Capacity of a Running Distillation Column

11.3 Capacity Diagram

11.4 Capacity Limitations of Distillation Columns

11.5 Vapour Handling Limitations. 11.5.1 Flow Regimes – Spray and Froth

11.5.2 Entrainment

11.5.3 Tray Flooding (Zhu, 2013)

11.5.4 Ultimate Capacity

11.6 Liquid Handling Limitations. 11.6.1 Downcomer Flood

11.6.2 Downcomer Residence Time

11.6.3 Downcomer Froth Back‐Up%

11.6.4 Downcomer Inlet Velocity

11.6.5 Weir liquid loading (Zhu, 2013)

11.6.6 Downcomer Sizing Criteria

11.7 Other Limitations and Considerations. 11.7.1 Weeping (Zhu, 2013)

11.7.2 Dumping

11.7.3 Tray Turndown

11.7.4 Foaming

11.8 Understanding the Stable Operation Zone (Zhu, 2013)

11.9 Case Study to Develop a Capacity Diagram

11.9.1 Calculation of Capacity Limits (Zhu, 2013) 11.9.1.1 Spray Limit

11.9.1.2 Vapor Flooding Limit (Zhu, 2013)

11.9.1.3 Downcomer Backup Limit (Zhu, 2013)

11.9.1.4 Maximum Liquid Loading Limit (Zhu, 2013)

11.9.1.5 Minimum Liquid Loading Limit

11.9.1.6 Minimum Vapor Loading Limit

11.9.2 Plotting a Capacity Diagram (Zhu, 2013)

11.9.3 Insights from the Capacity Diagram

11.9.4 How Can the Capacity Diagram Be Used for Profit Maximization?

References

12 New Design Methodology. 12.1 Need for New Design Methodology

12.2 Case Study of the New Design Methodology for a Distillation Column. 12.2.1 Traditional Way to Design a Distillation Column

12.2.2 Background of the Distillation Column Design

12.3 New Intelligent Methodology for Designing a Distillation Column

12.4 Problem Description of the Case Study

12.5 Solution Procedure Using the New Design Methodology

12.6 Calculations of the Total Cost

12.7 Search Optimization Variables

12.8 Operational and Hydraulic Constraints

12.9 Particle Swarm Optimization

12.9.1 PSO Algorithm

12.10 Simulation and PSO Implementation

12.11 Results and Analysis

12.12 Advantages of PSO

12.13 Advantages of New Methodology over the Traditional Approach (Lahiri, 2014)

12.14 Conclusion

Nomenclature

References

Appendix 12.1

13 Genetic Programing for Modeling of Industrial Reactors. 13.1 Potential Impact of Reactor Optimization on Overall Profit

13.2 Poor Knowledge of Reaction Kinetics of Industrial Reactors

13.3 ANN as a Tool for Reactor Kinetic Modeling

13.4 Conventional Methods for Evaluating Kinetics

13.5 What is Genetic Programming?

13.6 Background of Genetic Programming (Searson et al., 2011)

13.7 Genetic Programming at a Glance (Koza, 1992; Koza and Rice, 1992; Koza et al., 1999)

13.7.1 Preparatory Steps of Genetic Programming

13.7.2 Executional Steps of Genetic Programming

13.7.3 Creating an Individual

13.7.4 Fitness Test

13.7.5 The Genetic Operations

13.7.6 User Decisions

13.7.7 Computing Resources

13.8 Example Genetic Programming Run

13.8.1 Preparatory Steps

13.8.2 Step‐by‐Step Sample Run

13.8.3 Selection, Crossover, and Mutation

13.9 Case Studies

13.9.1 Case Study 1

13.9.2 Case Study 2

13.9.3 Case Study 3

13.9.4 Case Study 4

References

14 Maximum Capacity Test Run and Debottlenecking Study. 14.1 Introduction

14.2 Understanding Different Safety Margins in Process Equipment

14.3 Strategies to Exploit the Safety Margin

14.4 Capacity Expansion versus Efficiency Reduction

14.5 Maximum Capacity Test Run: What is it All About?

14.6 Objective of a Maximum Capacity Test Run

14.7 Bottlenecks of Different Process Equipment

14.7.1 Functional Bottleneck

14.7.2 Reliability Bottleneck

14.7.3 Safety Interlock Bottleneck

14.8 Key Steps to Carry Out a Maximum Capacity Test Run in a Commercial Running Plant

14.8.1 Planning

14.8.2 Discussion with Technical People

14.8.3 Risk and Opportunity

14.8.4 Dos and Don'ts

14.8.5 Simulations

14.8.6 Preparations

14.8.7 Management of Change

14.8.8 Execution

14.8.9 Data Collections

14.8.10 Critical Observations

14.8.11 Report Preparations

14.8.12 Detailed Simulations and Assembly of All Observations

14.8.13 Final Report Preparation

14.9 Scope and Phases of a Detailed Improvement Study

14.9.1 Improvement Scoping Study

14.9.2 Detail Feasibility Study

14.9.3 Retrofit Design Phase

14.10 Scope and Limitations of MCTR. 14.10.1 Scope

14.10.2 Two Big Benefits of Doing MCTR

14.10.3 Limitations of MCTR

15 Loss Assessment. 15.1 Different Losses from the System

15.2 Strategy to Reduce the Losses and Wastages

15.3 Money Loss Audit

15.4 Product or Utility Losses

15.4.1 Loss in the Drain

15.4.2 Loss Due to Vent and Flaring

15.4.3 Utility Loss

15.4.4 Heat Loss Assessment for the Fired Heater

15.4.5 Heat Loss Assessment for the Distillation Column

15.4.6 Heat Loss Assessment for Steam Leakage

15.4.7 Heat Loss Assessment for Condensate Loss

16 Advance Process Control. 16.1 What is Advance Process Control?

16.2 Why is APC Necessary to Improve Profit?

16.3 Why APC is Preferred over Normal PID Regulatory Control (Lahiri, 2017c)

16.4 Position of APC in the Control Hierarchy (Lahiri, 2017c)

16.5 Which are the Plants where Implementations of APC were Proven Very Profitable?

16.6 How do Implementations of APC Increase Profit?

16.7 How does APC Extract Benefits?

16.8 Application of APC in Oil Refinery, Petrochemical, Fertilizer and Chemical Plants and Related Benefits

16.9 Steps to Execute an APC Project (Lahiri, 2017d)

16.9.1 Step 1: Preliminary Cost –Benefit Analysis

16.9.2 Step 2: Assessment of Base Control Loops

16.9.3 Step 3: Functional Design of the Controller

16.9.4 Step 4: Conduct the Plant Step Test

16.9.5 Step 5: Generate a Process Model

16.9.6 Step 6: Commission the Online Controller

16.9.7 Step 7: Online APC Controller Tuning

16.10 How Can an Effective Functional Design Be Done?

16.10.1 Step 1: Define Process Control Objectives

16.10.2 Step 2: Identification of Process Constraints

16.10.3 Step 3: Define Controller Scope

16.10.4 Step 4: Variable Selection

16.10.5 Step 5: Rectify Regulatory Control Issues

16.10.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations

16.10.7 Step 7: Evaluate Potential Optimization Opportunity

16.10.8 Step 8: Define LP or QP Objective Function

References

17 150 Ways and Best Practices to Improve Profit in Running Chemical Plant. 17.1 Best Practices Followed in Leading Process Industries Around the World

17.2 Best Practices Followed in a Steam and Condensate System

17.3 Best Practices Followed in Furnaces and Boilers

17.4 Best Practices Followed in Pumps, Fans, and Compressor

17.5 Best Practices Followed in Illumination Optimization

17.6 Best Practices in Operational Improvement

17.7 Best Practices Followed in Air and Nitrogen Header

17.8 Best Practices Followed in Cooling Tower and Cooling Water

17.9 Best Practices Followed in Water Conservation

17.10 Best Practices Followed in Distillation Column and Heat Exchanger

17.11 Best Practices in Process Improvement

17.12 Best Practices in Flare Gas Reduction

17.13 Best Practices in Product or Energy Loss Reduction

17.14 Best Practices to Monitor Process Control System Performance

17.15 Best Practices to Enhance Plant Reliability

17.16 Best Practices to Enhance Human Resource

17.17 Best Practices to Enhance Safety, Health, and the Environment

17.18 Best Practices to Use New Generation Digital Technology

17.19 Best Practices to Focus a Detailed Study and R&D Effort

Index

WILEY END USER LICENSE AGREEMENT

Отрывок из книги

Sandip Kumar Lahiri

National Institute Of Technology, Durgapur, India

.....

Running the plant at maximum capacity does not mean to run it at its nameplate capacity, i.e. process flow diagram (PFD) capacity. That is the bare minimum target. All over the world, good companies are running at 125–150% of their nameplate design capacity. Normally they follow three basic steps to increase plant capacity:

All good plants follow these three steps in order and continuously improve themselves so that with the same plant they can run 25–50% extra capacity. This is one of the surest ways to increase profit.

.....

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