FACULTY OF AUTOMATION AND COMPUTER SCIENCES PHD THESIS

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1 Investeşte în oameni! FONDUL SOCIAL EUROPEAN Programul Operaţional Sectorial Dezvoltarea Resurselor Umane Axa prioritară: 1 Educaţia şi formarea profesională în sprijinul creşterii economice şi dezvoltării societăţii bazate pe cunoaştere Domeniul major de intervenţie: 1.5 Programe doctorale si postdoctorale în sprijinul cercetării Titlul proiectului: Proiect de dezvoltare a studiilor de doctorat în tehnologii avansate- PRODOC Cod Contract: POSDRU 6/1.5/S/5 Beneficiar: Universitatea Tehnică din Cluj-Napoca FACULTY OF AUTOMATION AND COMPUTER SCIENCES PHD THESIS RESEARCH ON THE DEVELOPMENT OF A GLOBAL OPTIMIZATION SYSTEM FOR INDUSTRIAL PROCESSES Eng. Andreea Raluca SAVU Supervisors: Prof.dr.eng. Gheorghe LAZEA Prof. dr. eng. Paul-Şerban AGACHI

2 Contents 1 INTRODUCTION Theses objectives Paper structure THEORY Advanced Process Control (APC) Integrated optimization in control systems RESEARCH ON THE DEVELOPMENT OF AN OPTIMIZATION SYSTEM FOR INDUSTRIAL PROCESSES Introduction Optimization system s structure Conclusions FIRST CASE THE THERMAL CRACKING PROCESS Introduction The pirolysis process The mathematical modelling of a pirolysis furnace The pirolysis process simulation Pirolysis furnace optimization The delayed coking process Mathematical modelling Delayed coking simulation The optimization of the delayed coking process Conclusions SECOND CASE STEAM POWER PLANT Introduction Steam power plant description Mathematical modelling using DeltaV and Excel Power plant simulation using DeltaV and Excel The optimization of a steam power plant or energy management system Conclusions FINAL CONCLUSIONS AND CONTRIBUTIONS REFERENCES... 8

3 1 INTRODUCTION 1.1 Theses objectives The main objective of this paper was the research on how to use a model predictive control technology (PredictPro), implemented in a fully digital automation system (DCS DeltaV TM ), to develop a structure capable of optimizing the functionality of any industrial plant. As its title suggests, the core of any MPC technology is the process model itself. So, the secondary objective of this work is the research on how to model and simulate certain industrial processes. In order to test the optimization system, two types of processes were studied: Thermal cracking processes and A steam power plant. 1.2 Paper structure The paper is composed of six chapters, starting with a theory introduction about advanced process control and integrated optimization (Chapter 2). The third chapter presents an optimization structure, built entirely in DeltaV Control Studio and using PredictPro. The utility of the system was proved using three different processes presented in Chapters 4 and 5. For using MPC, steady-state models were a minimum necessity. So, in this paper, a set of mathematical and empirical models were developed for these three processes, using high level programming languages (Matlab and Symulink ) and specialized software (Chemcad TM and DeltaV TM ). 2 THEORY 2.1 Advanced Process Control (APC) One of the most used APC technique is model predictive control (MPC) and along the years since its release it has been well accepted by the industry. DeltaV Predict represents a new generation of MPC solutions embedded in the DeltaV TM system. Predict s algorithm is based on the dynamic matrix control (DMC) technology. 1

4 2.2 Integrated optimization in control systems DeltaV PredictPro is another MPC tool from DCS DeltaV TM. It is capable to operate multivariable processes and its control objective function can be coupled with the optimization system. It allows the use of as much as five control and economic objective functions, without violating the constraints and taking into consideration the measurable disturbances. It is also based on the DMC technology, just like Predict, but has certain modifications that increase the robustness of the controller. 3 RESEARCH ON THE DEVELOPMENT OF AN OPTIMIZATION SYSTEM FOR INDUSTRIAL PROCESSES 3.1 Introduction Figure 1 Optimization system s architecture. This paper offers a global solution for some optimization problems, a system that provides a series of set points for the manipulated variables of certain industrial processes. The system was built in cooperation with the Performance Monitoring and Optimization Division, part of Emerson Process Management, UK. The purpose was the development of a software package, based on the MPCPro control block, able to optimize the functioning of any plant that respects certain conditions. The package consists of nine modules, having the MPCPro block placed in the centre, as it 2

5 can be observed in Figure 1. All the modules have been built in DeltaV Control Studio and using the DeltaV PredictPro technology. 3.2 Optimization system s structure The major modules and templates of the system are enumerated below. They are used to ease the link between the physical inputs of the real plant or simulator and the MPCPro controller: Input modules for each manipulated variable. Output modules used to simulate the constraint values for certain current manipulated variables (values from the real plant of manually set by an operator) Price modules they compute the span for each manipulated variable and for each constraint and sets the associated cost in percentage, inside the MPCPro block. Initialization module. Nonlinearities simulation module. OPTIMIZER module with the MPCPro block that has an embedded optimization tool, and some additional matrices used for the simulation of the optimized and non-optimized process and also in other modules. 3.3 Conclusions The system is capable of operating any industrial process towards the optimal operating point, as long as the process: Has no more than forty manipulated variables Has less than eighty associated constraints and It can be represented or approximated by a linear system. 4 FIRST CASE THE THERMAL CRACKING PROCESS 4.1 Introduction Cracking of hydrocarbons is a process in which big molecules are decomposed into smaller ones, under the influence of temperature and pressure, with or without a catalyst, with the aim of producing valuable products. The industrial thermal cracking processes are listed below: Pirolysis or steam cracking is used for the production of ethylene and propylene in the petrochemical industry. Visbreaking for producing liquid fuels form high petroleum residues. 3

6 Coking is used for obtaining coke from high petroleum residues. There are two types of coking processes: delayed coking and fluid coking. 4.2 The pirolysis process An ethylene producing plant is one of the largest plants in the petrochemical industry and the thermal cracking furnace is the centre of the entire production process. Ethane and naphtha are frequently used as feed in the pirolysis process, but propane, liquid petroleum gas (LPG), gaseous fuels, kerosene or vacuum residue are possible candidates as well. The desired reaction is represented by the decomposition of a hydrocarbon molecule from the feed into its olefinic equivalent. The free radical mechanism is universally accepted as an explanation for hydrocarbon pirolysis at low conversions and ethane cracking represents the simplest application of this mechanism The mathematical modelling of a pirolysis furnace The mathematical description of a one-dimensional plug-flow reactor tube consists of several balance equations, with the following assumptions: laminar regime, axial dispersion neglected, ideal gas behavior and inert steam diluents: Material balance: Energy balance: dt dz, Mechanical energy balance: (4.1) (4.2) (4.3) 2 1 (4.4) The differential equations were transformed, in order to be implemented in DeltaV. The so called Linked Composites were created, which are stored in the project database and can be reused as instances inside modules. Overall, the model was implemented using 18 classes and 5 modules. The results are detailed in (Savu and Lazea (2008)). 4

7 For the implementation of the cracking tube s mathematical model, the program Matlab was considered to be more appropriate sincet here are many possibilities to connect Matlab with DCS platforms. The feed was considered 97% ethane and the results were presented at the ESCAPE20 Conference (Savu et al., 2010). In order to compare the results with the ones from literature the feed was also considered composed of both ethane (55%) and propane (45%) The pirolysis process simulation For simulating the behaviour of a cracking furnace one can use Simulink blocks and a Matlab function that contains the implementation of the mathematical model (function [sys x0]=ex(t,x,u)), 4 inputs called u and 13 outputs called x. The model in the function is the one presented previously. If the purpose is the optimization of the process, then one can use an empirical model implemented in Excel to simulate the linear process Pirolysis furnace optimization The objective function was formulated as follows:, (4.5) Where - represents the income, reactants costs and - fixed costs. Four reactants were considered in the feed and the system from Chapter 3 was applied for the process, having seven manipulated variables and ten constraints. The results have been presented at the MED Conference (Savu et al., 2011). If we compare one optimization case with a base case we get a profit rate of about 1.03%, which represent a total profit of about $/year. 4.3 The delayed coking process The delayed coking process is the most important process of converting vacuum residue into valuable fuels. It is present in refineries all over the world Mathematical modelling The mathematical description of the furnace from the delayed cracking process is similar with the one presented previously. The minimum number of differential equations for material balance is equal to the number of independent reactions or the number of pseudocomponents, in this situation. 5

8 In order to model the fractionation column one can use the MESH equations (Material balances, Equilibrium relations, Summation equations -i.e. molar fractions sum to unity- and enthalpy balances) for each tray. The balance equations present in the model are: total mass, component mass, energy and vapour-liquid balance Delayed coking simulation In order to simulate such a complex process one can use commercial applications like HYSYS, ASPEN PLUS or Chemcad TM that allow almost real steady-state simulations. From the optimization point of view, the first two are too detailed, but Chemcad TM seems to be suitable for the purpose. The implemented simulator considers 52 pseudo-components in the process and it was built using predefined components like: distillation/ fractionation towers, furnaces, multipurpose flash, heat exchangers and so on The optimization of the delayed coking process This process is very difficult to control because there are many disturbances in the system generated by the coking chambers, which need preheating, but from the optimization system s perspective one can implement recipes based on the feed quality and customers demands, taking into consideration only the steady-state regime of the system. One option could be to maximize the quantity of all products, a second one to maximize the quantity of coke if the demand for high quality coke (needle coke) is high or to maximize only the quantities of resulted liquids. In this paper, four recipes were implemented in DeltaV and for one of them, the obtained profit rate is around 1.8% which represents a profit of approximately $/year. 4.4 Conclusions In conclusion, the personal contributions to this section of the paper consist of: The implementation of the mathematical models, using DeltaV and Matlab. The applied optimization system. 5 SECOND CASE STEAM POWER PLANT 5.1 Introduction A steam power plant is composed of several boilers, turbines, pressure reducing valves and steam headers. Such a power plant produces electric energy using steam turbines and the steam is obtained from boilers running on different types of fuels. 6

9 5.2 Steam power plant description Mathematical modelling using DeltaV and Excel In DeltaV Control Studio, templates were implemented in order to model each component of the plant and graphical interfaces were built in DeltaV Operate Configure, to test these models. Each component considers the thermo dynamical properties of steam (enthalpy and entropy), temperatures, pressures and volumes and efficiency curves. For validation, a similar model was implemented in Excel, using the same mathematical relations Power plant simulation using DeltaV and Excel The simulation was implemented using the structure from chapter 3 and some additional modules that handle different scenarios like: Turning on and off equipments: boilers, turbines, valves or fuel flows. Simulation of different functioning modes: base load, limited step, cascade/ non-cascade. Calculation of certain costs Graphical interfaces: AA_PLANT, PLANT_VALUES, NON_CAS, BASE_CASE and so on The optimization of a steam power plant or energy management system The optimization in this situation is considered to be an EMS energy management system. The structure from chapter 3 should determine the optimal loading of boilers and turbines, in order to satisfy the customer demand on steam and electricity. The objective function is defined below: _ _ _ _ (5.1) The system was tested on 39 different configurations, the most complex one reaching the MPCPro s limit for the number of manipulated variables: 40 MV and 28 constraints. If we compare a base case with an optimized one, the profit rate reaches the value of 1.83% which represents an annual profit of approximately $. 7

10 5.3. Conclusions The EMS s purpose is to run the power plant in an optimal manner, to satisfy the customer s demand and maximize the overall profit. The system was tested at its maximum and the results were satisfactory. 6 FINAL CONCLUSIONS AND CONTRIBUTIONS The primary objective of this paper was the research on how to use PredictPro, implemented in DCS DeltaV TM, to develop a structure capable of optimizing the functionality of any industrial plant. The optimization system presented in the third chapter is fully functional and the three studied cases demonstrate its utility and its capabilities. There is only one academic report on PredictPro s functionality (Rueda et al., 2006) but it presents only the control aspect of this tool, without any reference to the optimization capabilities. Personal contributions to this subject are related to: The development of an optimization structure, built entirely in DeltaV, able to operate several types of processes in an optimal manner. For testing the system graphical interfaces were developed in DeltaV Operate Configure. The implementation of mathematical models for cracking furnaces and power plant components, using Matlab, DeltaV and Excel. The simulations of cracking processes and a steam power plant using DeltaV, Excel, Simulink and Chemcad. 7 REFERENCES 1. Savu A.,(2008), Dezvoltarea modelului dinamic al unui cuptor de producere a etilenei şi implementarea lui în DeltaV. AplicaŃie Emerson Process Management Cluj- Napoca, Lucrare de diplomă. 2. Savu A., Muntean I., Lazea G. and Agachi P-Ş., (2011), Economic optimization of a thermal cracker via Model Predictive Control technology, The 19th Mediterranean Conference on Control and Automation, June 20-25, ISBN: , pp Savu A., Lazea G. and Agachi P-Ş.,(2010), Optimization and advanced control for thermal cracking processes, Computer - Aided Chemical Engineering, vol. 28, pp Rueda L., Edgar T.F. şi Eldridge R.B., (2006), Experimental Validation of Model-Based Control Strategies for Multicomponent Azeotropic Distillation, Proc. IFAC Symposium Advanced Control of Chemical Processes, ADCHEM, Gramado, Brazil, pp