A collaborative, multi-agent based methodology for abnormal events management

Ph.DDOCTOR OF PHILOSOPH

Using traditional modelling approaches for a MBR system to investigate alternate approaches based on system identification procedures for improved design and control of a wastewater treatment process.

The specific research work described in this thesis forms part of a much larger research project that was funded by the Technology Programme of the UK Government. This larger project considered improving the design and efficiency of membrane bioreactor (MBR) plant by using modelling, simulation and laboratory methods. This research work uses phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative behavioural models based upon input-output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are (e.g. using either a physically mechanistic model or an input-output behaviourial model). Each situation usually proves unique. In this regard, this research work creates a "Model Conceptualisation Procedure" for a typical MBR which can be used by future researchers as a theoretical framework which underpins any newly created model type. There has been insufficient work completed to date on using a times series input-output approach in the model development of a wastewater treatment plant, so only general conclusions can be made from this research work. However, it can be stated that this novel approach seems to be applicable for a membrane filtration model if care it taken to select appropriate input-output model structures, such as those suggested in the "Model Conceptualisation Procedure". In the case of the development of a MBR biological model, it is thought that a conventional Activated Sludge model produced by the IWA could be coupled to a input-output model structure as suggested by this report to give a hybrid model structure that may have the advantages of both model types. Further research work is needed in this area. Future work that should follow on from this research study should focus on whether these input-output models could be used for predictive control purposes, whether an integrated model could be created, and whether a benchmark could be created for the three main MBR configurations.Technology Programme of the TSB (Technology Strategy Board) TP/3/DSM/6/I/1512

Multilevel Monte Carlo for noise estimation in stochastic multiscale systems

The final publication is available at Elsevier via https://doi.org/10.1016/j.cherd.2018.10.006� 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The purpose of this study is to adapt Multilevel Monte Carlo (MLMC) sampling technique for random noise estimation in stochastic multiscale systems and evaluate the performance of this method. The system under consideration was a simulation of thin film formation by chemical vapour deposition, where a kinetic Monte Carlo solid-on-solid model was coupled with partial differential equations that represented mass, energy and momentum transport. The noise in the expected value of the system�s observable (film roughness) was estimated using MLMC and standard Monte Carlo (MC) sampling. The MLMC technique achieved conservative estimates of noise in the observable at an order of magnitude lower computational cost than standard MC sampling. This study highlights the nuances of adapting the MLMC technique to the stochastic multiscale system and provides insight on the benefits and challenges of using MLMC for noise estimation in stochastic multiscale systems.Natural Sciences and Engineering Research Council of Canad

Identification and energy optimization of supercritical carbon dioxide batch extraction

Abstract. The emergence of green chemistry, aiming to increase ecological and energy efficiency of processes, has gained supercritical fluid extraction increasing amounts of prominence. Traditional extraction methods utilize hazardous chemicals, have low extractive yield in relation to energy consumption, and produce large amounts of organic waste. Supercritical fluid extraction offers improvements to these challenges in the form of reduced processing energy inputs and an alternative solvent approach. Carbon dioxide is the most commonly employed solvent in supercritical fluid extraction due to the many advantages it brings over other solvents including price, smaller environmental and health risks, and simple separation. The research on data-driven system identification and advanced process control of supercritical extraction has been very scarce. According to past research, the control of supercritical is mostly carried out using basic, non-model-based control schemes. Challenges such as coupling between control loops and nonlinearities of fluid and process dynamics create major challenges for the basic control schemes. With advanced control methods, it could be possible to address these challenges better. Model-based control schemes, in theory, pose many advantages and benefits over basic control, such as improved production economics, optimized product quality and yields, and further possibilities in model-driven research and development. The goal of this thesis was to improve control performance and optimize energy consumption a pilot-scale batch supercritical carbon dioxide extraction process by utilizing model predictive control strategies. The modeling of the unit processes of the target batch extraction was based on measurement data gathered by experimental design and careful examination of the system. The models were utilized in a simulator developed in this study. The arrangement of the implemented experimental design (central composite design, CCD) allowed the exploitation of linear regression analysis; the results of which indicated the existence of possible nonlinearities between steady-state electricity consumption and the operative variables of the process. Model predictive control schemes were developed in a simulator environment for carbon dioxide pressure control, carbon dioxide volumetric flow control, extractor temperature control and separator temperature control. The developed control schemes showed major improvements in control performance of the simulated unit processes, resulting in significant decreases in total electricity and heating water consumptions (up to 25% and 21% respectively). Model predictive control also proved to be quite flexible over the base control system for some processes, providing the possibility of modifying control performance by simple tuning adjustments. The simulated control strategies demonstrate the benefits of model-based control in terms of process energy efficiency and economy. In addition to these results, the identified process and controller models have further potential in future research on control and process developments of supercritical fluid extraction.Ylikriittisen hiilidioksidipanosuuton identifiointi ja energiaoptimointi. Tiivistelmä. Prosessien ekologisuuden ja energiatehokkuuden lisäämiseen tähtäävä vihreä kemia edistää ylikriittisen uuton merkittävyyttä yhä enemmän. Perinteiset erotusmenetelmät käyttävät haitallisia kemikaaleja, niillä on alhainen uuteainesaanto suhteessa energian kulutukseen, ja ne tuottavat suuren määrän orgaanista jätettä. Ylikriittinen uutto tarjoaa parannuksia näihin haasteisiin prosessointienergian kulutuksen vähentymisen ja vaihtoehtoisen liuotinratkaisun muodossa. Hiilidioksidi on yleisimmin käytetty liuotin ylikriittisessä uutossa, koska sillä on monia etuja muihin liuottimiin verrattuna, mukaan lukien hinta, pienemmät ympäristö- ja terveysriskit sekä yksinkertainen erottaminen. Ylikriittiseen uuttoprosessiin liittyvän datapohjaisen identifioinnin ja kehittyneen säädön tutkimus on ollut hyvin vähäistä. Aiempien tutkimusten perusteella ylikriittisen uuton säätö toteutetaan pääasiassa perustason ei-mallipohjaisilla säätörakenteilla. Ohjaussilmukoiden vuorovaikutukset sekä neste- ja prosessidynamiikan epälineaarisuudet luovat suuria haasteita perussäätörakenteille. Kehittyneillä säätömenetelmillä olisi mahdollista käsitellä näitä haasteita paremmin. Mallipohjaiset säätöratkaisut tuovat teoriassa useita etuja ja hyötyjä perussäätöön verrattuna parantuvan tuotantoekonomian, optimoidun tuotelaadun ja -saannon sekä malliperusteisen tutkimuksen ja -kehityksen lisämahdollisuuksien muodossa. Tämän työn tavoitteena oli nostaa pilottikoon ylikriittisen hiilidioksidipanosuuttoprosessin säädön suorituskykyä ja optimoida energiankulutusta hyödyntämällä mallipredikriivisiä säätöstrategioita. Tutkimuksen kohteena olleen panosuuton yksikköprosessien mallinnus perustui koesuunnittelulla kerättyyn mittausaineistoon ja järjestelmän huolelliseen tarkkailuun. Malleja hyödynnettiin työssä kehitetyssä prosessisimulaattorissa. Toteutettu koessunnitelma (central composite design, CCD) mahdollisti lineaarisen regressioanalyysin hyödyntämisen, jonka tulokset osoittivat mahdollisten epälineaarisuuksien olemassaolon prosessin vakaan tilan sähkönkulutuksen ja operatiivisten muuttujien välillä. Malliprediktiiviset säätörakenteet kehitettiin simulaatioympäristössä hiilidioksidin paineen, hiilidioksidin tilavuusvirtauksen, uuttoreaktorin lämpötilan, ja erottajan lämpötilan säädöille. Kehitetyt säätörakenteet toivat suuria säätöparannuksia simuloituihin yksikköprosesseihin, johtaen merkittäviin vähennyksiin käyttösähkön- ja lämmitysveden kulutuksissa (vastaavat vähennykset 25 % ja 21 % saakka). Malliprediktiivinen säätö osoitti myös joustavuutensa perusäätöjärjestelmään verrattuna joissakin prosesseissa, mahdollistaen säätösuorituskyvyn modifioinnin yksinkertaisilla viritysmuutoksilla. Simuloidut säätöstrategiat havainnollistavat mallipohjaisen säädön mahdollisia hyötyjä prosessin energiatehokkuuden ja taloudellisuuden kannalta. Näiden tulosten lisäksi identifioiduilla prosessi- ja säädinmalleilla on lisäpotentiaalia tulevaisuuden ylikriittisen uuton säädön tutkimuksissa ja prosessikehityksissä

Strategies for Optimal Control of the Current and Rotation Profiles in the DIII-D Tokamak

The tokamak is currently the most promising device for realizing commercially-viable fusion energy production. The device uses magnetic fields to confine a circulating ring of hydrogen in the plasma state, i.e. a cloud of hydrogen ions and electrons. When sufficiently heated the hydrogen ions can overcome the electrostatic forces and fuse together, providing an overwhelmingly abundant energy source. However, stable, high-performance operation of a tokamak requires several plasma control problems to be handled simultaneously. Moreover, the complex physics which governs the tokamak plasma evolution must be studied and understood to make correct choices in controller design. In this thesis, two key control issues are studied intensely, namely the optimization and control of the plasma current profile and control of the plasma rotation (or flow).In order to maximize performance, it is preferable that tokamaks achieve advanced scenarios (AT) characterized by good plasma confinement, improved magnetohydrodynamic stability, and a largely non-inductively driven plasma current. A key element to the development of AT scenarios is the optimization of the spatial distribution of the current profile. Also, research has shown that the plasma rotation can stabilize the tokamak plasma against degradations in the desired MHD equilibrium.In this thesis, new model-based control approaches for the current profile and rotation profile are developed to allow experimental exploration of advanced tokamak scenarios. Methods for separate control of both the current profile and rotation are developed. The advanced model-based control methods presented in this thesis have contributed to theory of tokamak profile control and in some cases they have been successfully validated experimentally in the DIII-D tokamak

MATLAB

This excellent book represents the final part of three-volumes regarding MATLAB-based applications in almost every branch of science. The book consists of 19 excellent, insightful articles and the readers will find the results very useful to their work. In particular, the book consists of three parts, the first one is devoted to mathematical methods in the applied sciences by using MATLAB, the second is devoted to MATLAB applications of general interest and the third one discusses MATLAB for educational purposes. This collection of high quality articles, refers to a large range of professional fields and can be used for science as well as for various educational purposes