Module 8: “Introduction to Process Integration ”

1. Module 8: “Introduction to Process Integration” Program for North American Mobility in Higher Education (NAMP) Introducing Process Integration for Environmental Control in Engineering Curricula (PIECE) Created at: École Polytechnique de Montréal & Universidad de Guanajuato

2. Purpose of Module 8 What is the purpose of this module? This module is intended to covey the basic aspects of Process IntegrationMethods and Tools, and places Process Integration into a broad perspective. It will be identified as a pre-requisite for all other modules related to the learning of Process Integration.

3. Struture of module 8 What is the structure of this module? The Module 8 is divided into 3 “tiers”, each with a specific goal: • Tier 1: Background Information • Tier 2: Case Study Applications of Process Integration • Tier 3: Open-Ended Design Problem These tiers are intended to be completed in order. Students are quizzed at various points, to measure their degree of understanding, before proceeding. Each tier contains a statement of intent at the beginning, and a quiz at the end.

4. Tier 1: Background Information

5. Tier 1: Statement of intent Tier 1: Statement of intent: The goal is to provide a general overview of process integration tools, with a focus on it’s link with profitability analysis. At the end of Tier 1, the student should: • Distinguish the key elements of Process Integration. • Know the scope of each process integration tool. • Have overview of each process integration tool.

6. Tier 1: contents The tier 1 is broken down into three sections: 1.1 Introduction and definition of Process integration. 1.2 Overview of PI tools 1.3 An “around-the-world tour” of PI practitioners focuses of expertise At the end of this tier there is a short multiple-answer Quiz.

7. Outline 1.1 Introduction and definition of Process integration. 1.2 Overview of Process Integration tools 1.3 An “around-the-world tour” of PI practitioners focuses of expertise 1.1 Introduction and definition of Process integration. 1.2 Overview of Process Integration tools 1.3 An “around-the-world tour” of PI practitioners focuses of expertise

8. 1.1 Introduction and definition of Process integration.

9. introduction The president of your company probably does not know what process integration can do for the company......... .......... But he should. Let’s look at why?

10. A Very Brief History of Process Integration • Linnhoff started the area of pinch (bottleneck identification) at UMIST in the 60’s, focusing on the area of Heat Integration • UMIST Dept of Process Integration was created in 1984, shortly after the consulting firm Linnhoff-March Inc. was formed PI is not really easy to define…

11. From an Expert Meeting in Berlin, October 1993 Definition of process integration The International Energy Agency (IEA) definition of process integration "Systematic and General Methods for Designing Integrated Production Systems, ranging from Individual Processes to Total Sites, with special emphasis on the Efficient Use of Energy and reducing Environmental Effects"

12. Definition of process integration Later, this definition was somewhat broadened and more explicitly stated in the description of it’s role in the technical sector by this Implementing Agreement: "Process Integration is the common term used for the application of methodologies developed for System-oriented and Integrated approaches to industrial process plant design for both new and retrofit applications. Such methodologies can be mathematical, thermodynamic and economic models, methods and techniques. Examples of these methods include: Artificial Intelligence (AI), Hierarchical Analysis, Pinch Analysis and Mathematical Programming. Process Integration refers to Optimal Design; examples of aspects are: capital investment,energy efficiency, emissions, operability, flexibility, controllability, safety and yields. Process Integration also refers to some aspects of operation and maintenance". Later, based on input from the Swiss National Team, we have found that Sustainable Development should be included in our definition of Process Integration. Truls Gunderson, International Energy Agency (IEA) Implementing Agreement, “A worldwide catalogue on Process Integration” (jun. 2001).

13. Definition of process integration El-Halwagi, M. M., Pollution Prevention through Process Integration: Systematic Design Tools. Academic Press, 1997. “A Chemical Process is an integrated system of interconnected units and streams, and it should be treated as such. Process Integration is a holistic approach to process design, retrofitting, and operation which emphasizes the unity of the process. In light of the strong interaction among process units, streams, and objectives, process integration offers a unique framework for fundamentally understanding the global insights of the process, methodically determining its attainable performance targets, and systematically making decisions leading to the realization of these targets. There are three key components in any comprehensive process integration methodology: synthesis, analysis, and optimization.”

14. Definition of process integration Nick Hallale, Aspentech – CEP July 2001 – “Burning Bright Trends in Process Integration” “Process Integration is more than just pinch technology and heat exchanger networks. Today, it has far wider scope and touches every area of process design. Switched-on industries are making more money from their raw materials and capital assets while becoming cleaner and more sustainable”

15. Definition of process integration North American Mobility Program in Higher Education (NAMP)-January 2003 “Process integration (PI) is the synthesis of process control, process engineering and process modeling and simulation into tools that can deal with the large quantities of operating data now available from process information systems. It is an emerging area, which offers the promise of improved control and management of operating efficiencies, energy use, environmental impacts, capital effectiveness, process design, and operations management.”

16. Definition of process integration So What Happened? • In addition to thermodynamics (the foundation of pinch), other techniques are being drawn upon for holistic analysis, in particular: • Process modeling • Process statistics • Process optimization • Process economics • Process control • Process design

17. Modern Process Integration context Process integration is primarily regarded as process design (both new and retrofits design), but also involve planning and operation. The methods and systems are applied to continuous, semi-batch, and batch process. • Business objectives currently driving the development of PI: • Emphasis is on retrofit projects in the “new economy” driven by Return on Capital Employed (ROCE) • PI is “Finding value in data quality” • Corporations wish to make more knowledgeable decisions: • For operations, • During the design process.

18. Reducing COSTS POLLUTION ENERGY Increasing THROUGHPUT YIELD PROFIT Modern Process Integration context • Possible Objectives: • Lower capital cost design, for the same design objective • Incremental production increase, from the same asset base • Marginally-reduced unit production costs • Better energy/environmental performance, without compromising competitive position

19. Modern Process Integration context Among the design activities that these systems and methods address today are: • ProcessModeling and Simulation, and Validations of the results in order to have information accurate and reliable of the process. • Minimize Total Annual Cost by optimal Trade-off between Energy, Equipment and Raw Material • Within this trade-off: minimize Energy, improve Raw Material usage and minimize Capital Cost • Increase Production Volume by Debottlenecking • Reduce Operating Problems by correct (rather than maximum) use of Process Integration • Increase Plant Controllability and Flexibility • Minimize undesirable Emissions • Add to the joint Efforts in the Process Industries and Society for a Sustainable Development.

20. Summary of Process Integration elements • Improving overall plant facilities energy efficiency and productivity requires a multi-pronged analysis involving a variety of technical skills and expertise, including: • Knowledge of both conventional industry practice and state-of-the-art technologies available commercially • Familiarity with industry issues and trends • Methodology for determining correct marginal costs. • Procedures and tools for Energy, Water, and raw material Conservation audits • Process information systems Process Data Process knowledge PI systems & Tools

21. Definition of process integration In conclusion, process integration has evolved from Heat recovery methodology in the 80’s to become what a number of leading industrial companies and research groups in the 20th century regarding the holistic analysis of processes, involving the following elements: • Process data – lots of it • Systems and tools – typically computer-oriented • Process engineering principles - in-depth process sector knowledge • Targeting - Identification of ideal unit constraints for the overall process

22. Outline 1.1 Introduction and definition of Process integration. 1.2 Overview of Process Integration tools. 1.3 An “around-the-world tour” of PI practitioners focuses of expertise. 1.1 Introduction and definition of Process integration. 1.2 Overview of Process Integration tools 1.3 An “around-the-world tour” of PI practitioners focuses of expertise

23. 1.2 Overview of Process Integration Tools

24. 1.2 Overview of Process Integration Tools Business Model And SupplyChain Modeling. Real Time Optimization Pinch Analysis DataReconciliation Optimization by Mathematical Programming Stochastic Search Methods • Process Simulation • Steadystate • Dynamic Life Cycle Analysis Data-Driven Process Modeling Integrate Process Design and Control Process Data

25. Click here Click here Click here Click here Click here Click here Click here Click here Click here Click here 1.2 Overview of Process Integration Tools • Business Model • Supply Chain Managment. Real Time Optimization Pinch Analysis Reconciliation Data Optimization by Mathematical Programming Stochastic Search Methods • Process Simulation • Steady state • Dynamic Life Cycle Analysis Data-Driven Process Modeling Integrate Process Design and Control Process Data NEXT

26. Process Simulation

27. Process Simulation What is a model? “A model is an abstraction of a process operation used to build, change, improve, control, and answer questions about that process” Process modeling is an activity using models to solve problems in the areas of the process design, control, optimization, hazards analysis, operation training, risk assessment, and software engineering for computeraided engineering environments. Process modeling

28. Process Simulation Process Modeling Tools of process modeling System Theory Physics and Chemistry Computes Science Numerical Methods Application Statistics Process modeling is an understanding of the process phenomena and transforming this understanding into a model.

29. Process Simulation What is a model used for? Nilsson (1995) presents a generalized model, which, as depicted in the figure below, can be used for different basic problem formulations: Simulation, Identification, estimation and design. Input MODEL Output I O If the model is known, we have two uses for our model: Direct: Input is applied on the model, output is studied (Simulation) Inverse: Output is applied on the model, Input is studied

30. Process Simulation If both Input and Output are Known, we have three formulations (Juha Yaako, 1998): • Identification:We can find the structure and parameters in the model. • Estimation: If the internal structure of model is known, we can find the internal states in model. • Design: If the structure and internal states of model are known, we can study the parameters in model.

31. Process Simulation Demands set to models: • Accuracy  Requirements placed on quantitative and qualitative models. • Validity  Consideration of the model constraints. A typical model process is non-linear, nevertheless, non-linear models are linearized when possible, because they are easier to use and guarantee global solutions. • Complexity  Models can be simple (usually macroscopic) or detailed (usually microscopic). The detail level of the phenomena should be considered. • Computational  The models should currently regard computational orientation. • Robustness Models that can be used for multiple processes are always desired.

32. Process Simulation The figure below shows a comparison of input and output for a process and its model. Note that always n > m and k > t. A model does not include everything. n>m, and k>t. “All models are wrong, Some models are useful” George Box, PhD University of Wisconsin Input Output PROCESS X1, ..., Xn Y1, ..., Yk Input Output MODEL X1, ..., Xm Y1, ..., Yt In the process industry we find, two levels of models; Plant models, and models of unit operations such as reactor, columns, pumps, heat exchangers, tanks, etc.

33. Process Simulation Types of models: • Intuitive: the immediate understanding of something without conscious reasoning or study. This are seldom used. • Verbal: If an intuitive model can be expressed in words, it becomes a verbal model. First step of model development. • Causal: as the name implies, these model are about the causal relations of the processes. • Qualitative: These models are a step up in model sophistication from causal models. • Quantitative: Mathematical models are an example of quantitative models. These models can be used for (nearly) every application in process engineering. The problem is that these models are not documented or can be too costly to construct when there is not enough knowledge (physical and chemical phenomena are poorly understood). Sometimes the application encountered does not require such model sophistication. From Stochastic knowledge From first Principles

34. Process Simulation Simulation: “what if” experimentation with a model • Simulation involves performing a series of experiments with a process model. Input Output MODEL • Steady State • Snapshot • Algebraic equations X1, ..., Xm Y1, ..., Yt Input Output MODEL (t) • Dynamic • Movie (time functions) • Time is an explicit variable differential equations • Certain phenomena require dynamic simulation (e.g. control strategies, real time descition). X(t)1, ..., X(t)m Y(t)1, ..., Y(t)t

35. m2 m2 t t Process Simulation Staedy state simulation of a storage tank Dynamic simulation of a storage tank t = time m1 m1 Illustration: Simulation unit Hi-Limit Level M=constant Lo-Limit M=f(t) m2 m2(t) Acumulation = In - Out + Production - Consumption 0=In - Out + Production - Consumption

36. Process Simulation • The steady-state simulation does not solve time-dependent equations. The Subroutines simulate the steady-state operation of the process units ( operation subroutines) and estimate the sizes and cost the process units ( cost subroutines). • A simulation flowsheet, on the other hand, is a collection of simulation units(e.g., reactor, distillation columns, splitter, mixer, etc.), to represent computer programs (subroutines) to simulate the process units and areas to represent the flow of information among the simulation units represented by arrows.

37. Process Simulation • To convert from a process flowsheet to a simulation flowsheet, one replaces the process unit with simulation units (Models). For each simulation unit, one assigns a subroutine (or block) to solve its equations. Each of the simulators has a extensive list of subroutines to model and solve the equations for many process units. • The Dynamic simulation enables the process engineer to study the dynamic response of potential process design or the existent Process to typical disturbances and changes in operating conditions, as well as, strategies for the start up and shut down of the potential process design or existing process.

38. Process Simulation Differences between Steady State and Dynamic Simulation

39. Process Simulation • The Sequential Modular Strategy • flowsheet broken into unit operations (modules) • each module is calculated in sequence • problems with recycle loops • The Simultaneous Modular Strategy • develops a linear model for each unit • modules with local recycle are solved simultaneously • flowsheet modules are solved sequentially • The Simultaneous Equation-solving Strategy • describe entire flowsheet with a set of equations • all equations are sorted and solved together • hard to solve very large equations systems Solution Strategies

40. Process Simulation Why steady-state simulation is important: • Better understanding of the process • Consistent set of typical plant/facility data • Objective comparative evaluation of options for Return On Investment (ROI) etc. • Identification of bottlenecks, instabilities etc. • Perform many experiments cheaply once the model is built • Avoid implementing ineffective solutions

41. OPTIMIZATION of plant operations Online system ADVANCEMENT OF PLANT OPERATIONS/ OPERATIONAL SUPPORT / OPTIMIZATION Predictive simulation Optimal conditions EDUCATION, TRAINING CONTROL SYSTEM Operation training simulator DCS control logic Plant diagnosis system Quasi-online system PROCESS DESIGN / ANALYSIS Examination of operations Control strategies Advanced control systems Batch scheduling Off-line system Process Simulation Why dynamic simulation is important:

42. Challenges of simulation • Simulation is not the highest priority in the plant facilities • Production or quality issues take precedence • Hard to get plant facilities resources for simulation • “Up front” time required before results are available • Model must be calibrated, and results validated, before they can be trusted • At odds with “quarterly balance sheet culture” • May need to structure project to get some results out early NEXT

43. Data Reconciliation

44. Data Reconciliation Typical Objectives of Data Treatment. • Provide reliable information and knowledge of complete data for validation of process simulation and analysis • Yield monitoring and accounting • Plant facilities management and decision-making • Optimization and control • Perform instrument maintenance • Instrument monitoring • Malfunction detection • calibration • Detect operating problems • Process leaks or product loss • Estimate unmeasured values • Reduce random and gross errors in measurements • Detect steady states

45. Data treatment is critical for • Process simulation • Control and optimization • Management planning Data Reconciliation Business management INFORMATION Site & plant management Scheduling & optimization Advanced control Basic process control Data Treatment

46. Management planning Production Plant shutdown Equipment performance Modeling and Simulation Optimization Instrumentation design Instrument maintenance Overview Data Reconciliation Manual data On-line data Data Treatment Lab data

47. Data Reconciliation Typical Problems With Process Measurements • Measurements inherently corrupted by errors: • measurement faults • errors during processing and transmission of the measured signal • Random errors • Caused by random or temporal events • Inconsistency (Gross) errors • Caused by nonrandom events: instrument miscalibration or malfunction, process leaks • Non-measurements • Sampling restriction, measuring technique, instrument failure

48. Data Reconciliation Random errors • Features • High frequency • Unrepeatable: neither magnitude nor sign can be predicted with certitude • Sources • Power supply fluctuation • Signal conversion noise • Changes in ambient condition

49. Data Reconciliation Inconsistency (Gross error) • Features • Low frequency • Predictable: certain sign and magnitude • Sources • Caused by nonrandom events • Instrument related • Miscalibration or malfunction • Wear or corrosion of the sensors • Process related • Process leaks • Solid deposits

50. Data Reconciliation F Random errors Gross error Reliable value t Illustration Of Random & Gross Errors: • abnormality