Mekanistisen termohydraulisen mallinnustavan soveltaminen uudentyyppisten teollisten prosessien dynaamiseen simulointiin

The PDF file of the dissertation includes the summary part and also all five publications as full texts.The process and energy industries have a remarkable position in developing sustainable future. They play an important role in mitigating climate change. Whilst aiming at energy efficient, material recycling, and emission-free processes, the industrial systems are becoming more complex. Process automation is fundamental in confirming that also complex systems can be managed and operated in an easy and safe way. Dynamic system-wide process simulation is practically the only way to verify the interoperability of the process and control solutions before building up the system. For the systems in operation, it enables virtual realistic studies without disturbances or risks for the actual process or people. The qualitative research approach in this work is case study. The modelling and dynamic simulation software Apros is used in five distinct cases, which extend the modelling from traditional nuclear and conventional power plant applications to a board machine, a carbon dioxide capturing power plant, ship energy systems, a seawater desalination plant, and a molten salt based energy storage system. The methodology relies on mechanistic thermal-hydraulic modelling and dynamic simulation. Method development was performed to model and simulate the application specific unit operations and working fluids. The functionality of the basic methodology and the extensions are demonstrated in the cases. The results of the work can be used in research and commercial simulation projects. New unit operation models and improvements for the fluid property calculation provide a variety of new potential applications. The model validation results help to estimate prediction capability in similar applications. The simulation applications guide modellers to use the methodology in both the presented and new areas. Regarding the case-specific results, the board machine simulator helped to understand complex interactions related to grade changes, to tune the related automation, and thus to shorten the grade change times. The simulation of the ship energy systems revealed design deficiencies and assisted in troubleshooting related problems during the commissioning. The study on the thermal energy storage facility uncovered systematic anomalous behaviour in the molten salt flow path. Based on the cross-case analysis, it can be stated that the methodology can be successfully applied beyond its traditional application domain and that it provides meaningful and valuable benefits. Furthermore, the methodology supports versatile use of the simulation model during the life cycle of an industrial plant: in R&D, design, testing, operator training and further development of the operating plant. The challenges that the process and energy industries meet today, require consideration of the interactions and dynamics of the process and automation systems together. The methodology used and further extended provides a valuable tool for tackling these challenges.Prosessi- ja energiateollisuudella on suuri merkitys kestävässä kehityksessä. Niillä on merkittävä rooli ilmastonmuutoksen hillinnässä. Pyrittäessä energiatehokkuuteen, materiaalien kierrätykseen ja päästöttömiin prosesseihin tulee teollisista järjestelmistä monimutkaisia. Prosessiautomaatiolla on keskeinen rooli siinä, että monimutkaisiakin järjestelmiä voidaan hallita ja käyttää helposti ja turvallisesti. Dynaaminen laitosmittakaavan prosessisimulointi on käytännössä ainoa tapa testata ja varmistaa prosessin ja automaation yhteistoiminta ennen kohdejärjestelmän rakentamista. Käytössä olevissa laitoksissa sen avulla voidaan tutkia järjestelmiä todenmukaisesti aiheuttamatta häiriötä tai riskiä prosessille tai ihmisille.  Tässä tapaustutkimuksena toteutetussa työssä käytetään Apros-ohjelmistoa mallinnus- ja simulointiympäristönä. Mallinnusta ja simulointia laajennetaan perinteisiltä ydin- ja konventionaalisten voimalaitosten sovellusalueilta kartongin valmistukseen, hiilidioksidia talteen ottavaan voimalaitokseen, laivan energiajärjestelmiin, meriveden suolanpoistoon sekä sulasuolaa käyttävään lämpövarastoon. Perusmenetelmänä hyödynnetään mekanistisia malleja ja termohydraulista dynaamista simulointia. Menetelmäkehitystä tehtiin sovelluskohtaisten laitteiden ja fluidien mallintamiseksi. Käytetyn menetelmän ja tehtyjen laajennusten toimivuus demonstroidaan simulointisovelluksissa. Työn tuloksia voidaan hyödyntää sekä tutkimuksessa että kaupallisissa simulointiprojekteissa. Uudet laitemallit ja fluidilaskennan ominaisuudet mahdollistavat uusia sovelluskohteita termisten järjestelmien parissa. Laskennan ja mallien validointitulokset auttavat arvioimaan saman tyyppisten mallien ennustuskykyä. Menetelmän hyödyntäminen sekä esitellyillä että uusilla sovellusalueilla tehostuu esimerkkimallien avulla. Tapauskohtaisista tuloksista voidaan mainita, että simulaattori auttoi ymmärtämään kartonkikoneen lajinvaihtoihin liittyviä monimutkaisia vuorovaikutuksia. Uudelleenvirittämällä lajinvaihtoautomaatio lyhennettiin lajinvaihtoihin kuluvaa aikaa. Laivan energiajärjestelmien simulointi paljasti suunnittelun puutteellisuuksia ja auttoi käyttöönoton ongelmien tutkimisessa. Sulasuolaa käyttävän, lämmönsiirron ja varastoinnin tutkimusta tukevan laitteiston toiminnasta analysoitiin systemaattinen poikkeama.  Tapausten analysoinnin perusteella voidaan todeta, että käytetty mallinnusmenetelmä soveltuu hyvin myös perinteisen sovellusalueensa ulkopuolella ja tuo merkittäviä hyötyjä. Menetelmä tukee simulointimallien monipuolista hyödyntämistä teollisuuslaitoksen elinkaaren aikana: tutkimuksessa, suunnittelussa, testauksessa, käyttäjien koulutuksessa sekä toimivan laitoksen kehittämisessä. Teollisuuden suunnittelun ja laitosten kasvavia haasteita on kyettävä ratkaisemaan eri elinkaaren vaiheissa prosessin ja automaation yhteistoiminta ja dynamiikka huomioiden. Työssä sovellettu ja laajennettu mallinnus- ja simulointimenetelmä tarjoaa tähän hyödyllisen työkalun

Neural network based hybrid modelling and MINLP based optimisation of MSF desalination process within gPROMS: Development of neural network based correlations for estimating temperature elevation due to salinity, hybrid modelling and MINLP based optimisation of design and operation parameters of MSF desalination process within gPROMS

Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand

Development of an Excel Based Spreadsheet of Analytical Hierarchy Process And Decision Making Grid For Maintenance Policy Decision

Maintenance policies are created to fulfill the company needs to ensure smooth and continuous operation. In Lean Manufacturing, the importance of an effective maintenance program cannot be overlooked. Since most of the industries used machinery in their plant, of course there must be proper maintenance to ensure continuous production and smooth operation. Maintenance policies such as Preventive Maintenance (PM), Corrective Maintenance (CM) and Condition Based Maintenance (CBM) are widely used as a way to solve maintenance problems. Maintenance selection can be very hard and complex when there are a lot of criteria that need to be considered since their importance are nearly significant to each other. Selecting the proper maintenance strategy can ensure high system’s reliability and availability. Decision Making Grid (DMG) and Analytical Hierarchy Process (AHP) are often used to identify strategies for maintenance decision. Automation using these methods through specialized software is very costly. Therefore, a cheaper alternative is needed. Two Excel spread sheets are developed by applying the formula for calculating AHP and DMG. One of the main objective of this project is to produce an integrated decision making tool depending on available data and depth of analysis. Validation is done by inserting data from selected research papers then compared to their actual value which is obtained from the datum. For DMG model, after inserting the inputs, the results are displayed on the DMG grid view. Based from the validation of data using case studies, it can be found that some of the actual data from the paper has inaccurate and incorrect results due to mistakes in calculations. Others are validated and both the tools and case studies produced the same result. Therefore, the tools are ready to use. If all of the steps for the development of the spread sheet are followed, the best maintenance policy can be selected by using both of these models. The user can select either to choose AHP or DMG as their decision making tool

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Design and Operation of Multistage Flash (MSF) Desalination: Advanced Control Strategies and Impact of Fouling. Design operation and control of multistage flash desalination processes: dynamic modelling of fouling, effect of non-condensable gases on venting system design and implementation of GMC and fuzzy control

The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant

Simulation, optimisation and flexible scheduling of MSF desalination process under fouling. Optimal design and operation of MSF desalination process with brine heater and demister fouling, flexible design operation and scheduling under variable demand and seawater temperature using gPROMS.

Among many seawater desalination processes, the multistage flash (MSF) desalination process is a major source of fresh water around the world. The most costly design and operation problem in seawater desalination is due to scale formation and corrosion problems. Fouling factor is one of the many important parameters that affect the operation of MSF processes. This thesis therefore focuses on determining the optimal design and operation strategy of MSF desalinations processes under fouling which will meet variable demand of freshwater. First, a steady state model of MSF is developed based on the basic laws of mass balance, energy balance, and heat transfer equations with supporting correlations for physical properties. gPROMS software is used to develop the model which is validated against the results reported in the literature. The model is then used in further investigations. Based on actual plant data, a simple dynamic fouling factor profile is developed which allows calculation of fouling factor at different time (season of the year). The role of changing brine heater fouling factor with varying seawater temperatures (during the year) on the plant performance and the monthly operating costs for fixed water demand and fixed top brine temperature are then studied. The total monthly operation cost of the process are minimised while the operating parameters such as make up, brine recycle flow rate and steam temperature are optimised. It was found that the seasonal variation in seawater temperature and brine heater fouling factor results in significant variations in the operating parameters and operating costs. The design and operation of the MSF process are optimized in order to meet variable demands of freshwater with changing seawater temperature throughout the day and throughout the year. On the basis of actual data, the neural network (NN) technique has been used to develop a correlation for calculating dynamic freshwater demand/consumption profiles at different times of the day and season. Also, a simple polynomial based dynamic seawater temperature correlation is developed based on actual data. An intermediate storage tank between the plant and the client is considered. The MSF process model developed earlier is coupled with the dynamic model for the storage tank and is incorporated into the optimization framework within gPROMS. Four main seasons are considered in a year and for each season, with variable freshwater demand and seawater temperature, the operating parameters are optimized at discrete time intervals, while minimizing the total daily costs. The intermediate storage tank adds flexible scheduling and maintenance opportunity of individual flash stages and makes it possible to meet variable freshwater demand with varying seawater temperatures without interrupting or fully shutting down the plant at any-time during the day and for any season. Finally, the purity of freshwater coming from MSF desalination plants is very important when the water is used for industrial services such as feed of boiler to produce steam. In this work, for fixed water demand and top brine temperature, the effect of separation efficiency of demister with seasonal variation of seawater temperatures on the final purity of freshwater for both cleaned and fouled demister conditions is studied. It was found that the purity of freshwater is affected by the total number of stages. Also to maintain the purity of freshwater product, comparatively large number of flash stage is required for fouled demister

Workshop Report: Developing a Research Agenda for the Energy Water Nexus

The energy water nexus has attracted public scrutiny because of the concerns about their interdependence and the possibility for cascading vulnerabilities from one system to the other. There are trends toward more water-­‐intensive energy (such as biofuels , unconventional oil and gas production, and regulations driving more water consumption for thermoelectric power production ) and more energy-­‐intensive water (such as desalination, or deeper ground water pumping and production). In addition demographic trends of population and economic growth will likely drive up total and per capita water and energy demand, and due to climate change related distortions of the hydrologic cycle, it is expected that the existing interdependencies will be come even more of a concern. Therefore, developing a research agenda and strategy to mitigate potential vulnerabilities and to meet economic and environmental targets for efficiently using energy and water would be very worthwhile. To address these concerns, the National Science Foundation (NSF) sponsored a workshop on June 10-­‐11, 2013 in Arlington, VA (at NSF headquarters) to bring together technical, academic, and industry experts from across the country to help develop such a research agenda. The workshop was sponsored by NSF Grant Number CBET 1341032 from the Division of Chemical, Bioengineering, Environmental and Transport Systems. Supporting programs were: Thermal Transport Processes, Environmental Sustainability, and Environmental Engineering.Center for Research in Water Resource

Control systems of offshore hydrogen production by renewable energies

Esta tesis trata sobre un proyecto de diseño de un Sistema de Gestión de Energía (SGE) que utiliza Modelo de Control Predictivo (MPC) para equilibrar el consumo de energía renovable con electrolizadores productores de hidrógeno. La energía generada se equilibra regulando el punto de operación y las conexiones de los electrolizadores usando un MPC basado en un algoritmo de Programación Mixta-Entera Cuadrática. Este algoritmo MPC permite tener en cuenta previsiones de energía, mejorando así el equilibrio y reduciendo el número de encendidos de los equipos. Se han realizado diferentes casos de estudio en instalaciones compuestas por unidades de generación de energía eléctrica a partir de energía renovable. Se considera la técnica de ósmosis inversa como paso intermedio para la producción de agua que alimenta a los electrolizadores. La validación se realiza utilizando datos meteorológicos medidos en un lugar propuesto para el sistema, mostrando el funcionamiento adecuado del SGE desarrollado.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Modelling, simulation and advanced control of small-scale reverse osmosis desalination plants

Esta tesis trata sobre el modelado, simulación y control avanzado de plantas de desalinización, basadas en ósmosis inversa. En primer lugar, se ha desarrollado una nueva librería dinámica de simulación de desalinizadoras, utilizando primeros principios, ecuaciones físico-químicas y correlaciones bibliográficas. Dicha librería se enfoca en los aspectos dinámicos del proceso, y es complementaria a las librerías presentes en el mercado. En segundo lugar, se han aplicado técnicas de modelado multiescala, para el modelado del proceso de la desalinización. A continuación, se ha estudiado el control avanzado de una desalinizadora, alimentada con energías renovables (placas solares y turbinas eólicas), desde un nuevo punto de vista. Englobando al mismo tiempo, la operación de la planta, las limpiezas periódicas, y la producción de energía eléctrica para su funcionamiento. Finalmente, se ha estudiado el diseño integrado de la planta, y la planificación de la operación para largos periodos de tiempo (varios años).Departamento de Ingeniería de Sistemas y Automátic