플라즈마 식각 장치를 위한 적응모델예측제어기의 설계

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2019. 2. 이윤우.The semiconductor etching process, which is one of the most critical processes in the manufacturing of semiconductors and one that comprises numerous steps, requires higher sophistication as 10 nm semiconductors are mass produced. Currently, the semiconductor etching process is mostly done by physical and/or chemical etching with plasma. In addition, the plasma etching is getting increasingly popular with the miniaturization of the process to a scale of less than 10 nm. The result of a plasma etching process is represented in the form of an etch profile which is determined by the plasma variables. Therefore, the performance of the process depends on these variables, and it is essential to measure and control them in real time. Although research on the control of plasma etching processes has been actively carried out, the plasma etching process strongly relies on the experience and skill level of seasoned engineers at the industry level. This is because a plasma-based system is very complicated and sensitive, and has a time-varying characteristics. However, even though previous studies show excellent results, they employed invasive diagnostic tools, and have single variable control schemes where a counter change of another plasma variable during control actions for other variables might occur due to the highly interactive plasma characteristics. Moreover, they did not consider the time-varying characteristics of plasma-based systems. Therefore, this thesis proposes a multivariable control strategy which can cope with interaction effects and a design of an adaptive model predictive controller which maintains its performance wherein systems vary with time. At first, the plasma variables which are considered as controlled variables were selected as the electron density and the electron temperature. This is because one of the etch profile, especially etch rate, can be expressed as functions of those plasma variables and the variables can be measured by the optical emission spectroscopy which is a non-invasive diagnostic tool. The plasma variables were paired with instrumental variables through singular value decomposition and relative gain array for constructing the optimal multivariable system model. Two parallel proportional integral derivative (PID) controllers were designed based on the output errors then conducted plasma variable control simulations. Through the simulations, the exist of interaction between the variables was obviously verified. For resolving the interaction effectively, decoupler controllers were applied to the PID controllers. As it performed the control experiment of the Ar plasma electron density and electron temperature excellently, the possibility of multivariable control of plasma-based system is demonstrated. In spite of the meaningful control results using the PID controllers, there are obvious limitations in relation to the exquisiteness and to the characteristics of decoupler controllers as it highly dependent to the accuracy of the system model. In order to maintain performance even in the case of a system change, an advanced control strategy is required and model predictive control can be an alternative. Therefore, a model predictive controller was designed where the Bayesian optimization is used as tuning method for the maximization of the exquisiteness. The controller verified its capability as it conducted Ar plasma electron density control in a drift-free system. However, the performance of it deteriorated in control simulations of time-varying systems and in a control experiment performed in a system where O2 plasma was injected into an Ar plasma system inducing the high nonlinearity. Therefore, a more advanced control strategy which can overcome the difficulty was required. In an adaptive control method, once the information from the system is entered into the adjustment mechanism part, the part makes a decision to deliver it to the controller. Then the controller is modified in accordance with the decision and releases the optimal control action. The typical adaptive control algorithm, which is the recursive least squares algorithm, was used in this thesis. Using the algorithm with Kalman filter interpretation, the time-delay effect which comes from the plasma etching reactor can be considered. The recursive model parameter estimator utilizing this algorithm was tuned by Bayesian optimization. When the recursive model parameter estimator detects changes of the system model parameters in real time and transmits it to the model predictive controller, the controller calculates the optimal manipulated variable based on the modified model. The adaptive model predictive controller performed the same simulations and experiment as those performed by the model predictive controller. Unlike the model predictive controller, the proposed controller performed control excellently even when the system changes over time. Numerically, it showed the improved control ability by 24.7% and 30.4% in terms of the mean absolute percentage error and the number of deviated sample, respectively compared to the conventional model predictive controller. These results demonstrate that the adaptive model predictive controller designed in this theses is invaluable for plasma-based system control and is the effective controller for the plasma etching reactor. It is expected to make a significant contribution to plasma-based processes that require high sophistication and flexibility.수 많은 공정으로 이루어진 반도체 제조 공정 내에서 가장 큰 비중을 차지하고 있는 반도체 식각 공정은 최근 10 nm급 반도체의 양산이 이뤄짐에 따라 식각의 높은 정교성이 요구되고 있다. 반도체 식각 공정은 현재 산업계에선 플라즈마를 이용하여 물리적, 화학적 식각을 일으키는 방법을 통해 이루어지고 있으며, 공정이 10 nm 급 이하 스케일로 미세화된 후로 이 방법이 더욱 각광 받고 있다. 공정의 결과는 식각 프로필을 기준으로 결정되는 데 이 식각 프로필이 플라즈마 변수들에 크게 의존함이 입증됨에 따라 이 변수들을 실시간으로 측정하고 제어하는 것이 공정의 핵심이 되었다. 그동안 플라즈마 변수 제어에 관한 연구들이 활발히 진행되어 왔으나 아직까지 산업계에선 그 이론들을 바로 활용하지 못하고 경험 많은 엔지니어의 감에 의존하고 있다. 그 이유는 근본적으로 시스템이 매우 복잡하고 예민할 뿐 아니라 시간에 따라 변하는 특성을 갖고 있기 때문이다. 그러나 이전의 연구들은 훌륭한 결과를 보였음에도 불구하고, 플라즈마 시스템에 직접적으로 영향을 미치는 침투성 센서를 이용했거나, 플라즈마 변수들과 장치 변수들이 서로 복잡하게 얽혀 있어 야기되는 상호작용을 간과할 수밖에 없는 단변수 제어를 수행한 데에 그치고 있다. 게다가 외란 때문에 발생되는 시간에 따라 변하는 특성을 고려하지 못하고 있다. 따라서, 본 학위논문에서는 변수간 상호작용을 최소화하는 다변수 제어 전략과 시스템이 시간에 따라 변하는 상황에서도 성능이 악화되지 않는 적응모델예측제어기의 설계를 제안한다. 먼저, 전자 밀도와 전자 온도가 제어 대상이 되는 플라즈마 변수로 선정되었다. 이는 식각 프로필, 특히 식각률이 이 변수들에 대한 함수로 표현될 수 있으며, 이 변수들은 침투성 센서인 광학적 발광 분광법을 통해 측정될 수 있기 때문이다. 그 다음에, 최적의 다변수 시스템 정의를 위해 특이치 분석과 상대이득배열을 이용하여 가장 효과적으로 제어를 수행할 수 있는 장치 변수 선정이 이루어졌다. 이를 바탕으로 두 개의 병렬로 연결된 비례적분미분제어기를 설계, 아르곤 플라즈마 전자 밀도와 전자 온도의 제어 시뮬레이션을 수행하였다. 해당 시뮬레이션에서 변수들 간 상호 작용이 확연함을 입증하였으며 이를 효과적으로 해결하기 위해 디커플러 제어기가 비례적분미분제어기에 결합되었다. 이 제어기는 아르곤 플라즈마의 전자 밀도와 전자 온도 제어를 훌륭하게 수행함으로써 다변수 플라즈마 시스템의 제어 가능성을 분명하게 입증하였다. 다변수 플라즈마 시스템의 제어 가능성이 입증 됐음에도 불구하고, 이 제어 전략은 비례적분미분제어기의 정교성 측면에서의 한계와 디커플러 제어기의 시스템 모델에 대한 높은 의존도 특성으로 인한 한계가 존재한다. 시스템이 변하는 상황에서도 성능을 유지하기 위해선 더욱 수준 높은 제어 전략이 요구되며, 모델예측제어가 그 대안이 될 수 있다. 모델예측제어기의 설계는 제어의 정교성을 극대화 시키기 위해 베이시안 최적화 기법을 통해 이루어졌다. 이 모델예측제어기는 인위적인 외란이 적용되지 않은 순수 아르곤 플라즈마 시스템에서의 전자 밀도 제어를 훌륭하게 수행함으로써 그 성능을 입증하였다. 하지만, 시스템이 시간에 따라 변하는 상황을 모사한 시뮬레이션과 산소 플라즈마가 아르곤 플라즈마 시스템에 주입되어 시스템 변화를 야기시키는 상황에서 수행된 제어 실험에서 제어기의 성능이 확연히 악화됨을 확인할 수 있었다. 따라서 이를 극복할 수 있는 더욱 발전된 제어 전략이 요구되었다. 적응 제어 기법은 시스템에서 얻어진 정보를 조절 메커니즘 부분으로 보내 실시간으로 제어기의 수정 사항을 결정하여 이를 바탕으로 제어를 수행하는 기법이다. 본 학위논문에서는 대표적인 적응 제어 알고리즘인 순환형 최소자승법 알고리즘을 활용하였다. 이 알고리즘에 칼만 필터 해석을 접목시킴에 따라, 플라즈마 식각 장치로부터 비롯되는 시간 지연의 효과를 고려할 수 있게 하였다. 이 알고리즘이 탑재된 순환형 모델 파라미터 추정기는 베이시안 최적화 기법을 통해 튜닝되었다. 순환형 모델 파라미터 추정기가 실시간으로 감지하는 모델 파라미터의 변화를 모델예측제어기에 전달하면 수정된 모델을 기반으로 제어기는 최적의 조절 변수를 계산한다. 이렇게 설계된 적응모델예측제어기는 앞서 모델예측제어기가 수행한 것과 동일한 시뮬레이션과 실험을 수행하였다. 모델예측제어기와 달리 적응모델예측제어기는 시간에 따라 시스템이 변하는 상황에서도 훌륭한 제어를 수행하였으며, 평균절대오차율을 기준으로 했을 때 기존의 모델예측제어기보다 24.7%의 향상된 제어 성능을 보여주었다. 이 결과는 본 학위논문에서 제안하고 있는 적응모델예측제어기가 시스템의 변화가 빈번한 플라즈마 시스템에서의 제어에 매우 가치 있음과 더불어 플라즈마 식각 장치에 유효한 제어기라는 것을 반증한다. 이 결과가 플라즈마 기반 시스템을 대상으로 하는 모든 제어 공정의 발전에 크게 이바지할 것을 기대하는 바이다.Abstract i Contents v List of Figures viii List of Tables xii CHAPTER 1. Introduction 1 1.1. Research motivation 1 1.2. Research objectives 4 1.3. Description of the equipment used in this thesis 5 1.4. Outline of the thesis 9 CHAPTER 2. Design of Multi-Input Multi-Output Controller for Plasma-based System 10 2.1. Introduction 10 2.2. Theoretical backgrounds 13 2.2.1. Estimation of plasma variables from optical emission spectroscopy 13 2.2.2. The influence of oxygen in plasma etching reactor 16 2.2.3. Singular value decomposition and condition number 18 2.2.4. Relative gain array method 21 2.2.5. Multi-loop control system 23 2.3. MIMO control results in the Ar plasma system 31 2.3.1. Variable selection and pairing 31 2.3.2. Disturbance rejection control results for SISO systems 37 2.3.3. Simulation of multi-loop control scheme and decoupling control scheme 41 2.3.4. Set-point tracking control experiment of multi-loop controller with decoupler controllers 58 2.4. Concluding remarks 62 CHAPTER 3. Disturbance Rejection Control of Plasma Electron Density by Model Predictive Controller 64 3.1. Introduction 64 3.2. Model predictive control 68 3.2.1. Concept of model predictive control 68 3.2.2. Description of model predictive control algorithm 71 3.2.2.1. State estimation algorithm 71 3.2.2.2. Optimization algorithm 76 3.3. Design of model predictive controller for Ar plasma system 78 3.3.1. System identification of Ar plasma system 78 3.3.2. Optimal MPC weight parameters from integral squared error based Bayesian optimization 80 3.3.3. Experimental results of Ar plasma electron density control 84 3.4. Disturbance rejection control using model predictive controller 86 3.4.1. Development of time-varying system model for control simulation 86 3.4.2. Design of model predictive controller for disturbance rejection control 91 3.4.3. Experimental result of disturbance rejection control in Ar/O2 plasma system 101 3.5. Concluding remarks 104 CHAPTER 4. Design of Adaptive Model Predictive Controller for Plasma Etching Reactor 106 4.1. Introduction 106 4.2. Recursive model parameter estimation 112 4.2.1. Recursive least squares algorithm with forgetting factor 113 4.2.2. Recursive least squares algorithm with Kalman filter interpretation 116 4.3. Adaptive model predictive control algorithm 119 4.4. Time-varying system control using adaptive model predictive controller 123 4.4.1. Initial system identification (Scaling method) 123 4.4.2. Design of adaptive model predictive controller for time-varying system 125 4.4.3. Set-point tracking control results in drifted system 143 4.5. Concluding remarks 152 CHAPTER 5. Conclusion 154 5.1. Summary of contributions 154 5.2. Future work 157 Nomenclature 159 References 167 Abstract in Korean (국문초록) 174Docto

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Adaptive control and identification for on-line drug infusion in anaesthesia.

Anaesthesia is that part of the medical science profession which ensures that the patient’s body is insensitive to pain and possibly other stimuli during surgical operations. It includes muscle relaxation (paralysis) and unconsciousness, both conditions being crucial for the operating surgeon. Maintaining a steady level of muscle relaxation as well as an acceptable depth of anaesthesia (unconsciousness), while keeping the dosage of administered drugs which induce those effects at a minimum level, have successfully been achieved using automatic control. Fixed gain controllers such as P, PI, and PID strategies can perform well when used in clinical therapy and under certain conditions but on the other hand can lead to poor performances because of the large variability between subjects. This is the reason which led to the consideration of adaptive control techniques which seemed to overcome such problems. Two control strategies falling into the above scheme and including the two newly developed techniques, i.e Proportional-Integral-Plus (PIP) control algorithm, and Generalized Predictive Control algorithm (GPC), are considered under extensive simulation studies using the muscle relaxation process associated with two drugs known as Pancuronium-Bromide and Atracurium. Both models exhibit severe non-linearities as well as time-varying dynamics and delays. Only the strategy corresponding to the GPC algorithm is retained for implementation on a 380Z disk-based microcomputer system, while the muscle relaxation process corresponding to either drugs is simulated on a VIDAC 336 analogue computer. The sensitivity of the algorithm is investigated when patient-to-patient parameter variability is evoked. The study is seen to provide the necessary basis for future clinical implementation of the scheme. Following the satisfactory results obtained under such a real-time environment, the self-adaptive GPC algorithm has been successfully applied in theatre to control Atracurium infusion on humans during surgery. This success later motivated further research work in which simultaneous control of muscle relaxation and anaesthesia (unconsciousness) was achieved. A good multivariable model has been derived and controlled via the multivariable version of the SISO GPC algorithm. The results obtained are very encouraging

Innovative Surveillance and Process Control in Water Resource Recovery Facilities

Water Resource Recovery Facilities (WRRF), previously known as Wastewater Treatment Plants (WWTP), are getting increasingly complex, with the incorporation of sludge processing and resource recovery technologies. Along with maintaining a stringent effluent water quality standard, the focus is gradually shifting towards energy-efficient operations and recovery of resources. The new objectives of the WRRF demand an economically optimal operation of processes that are subjected to extreme variations in flowrate and composition at the influent. The application of online monitoring, process control, and automation in WRRF has already shown a steady increase in the past decade. However, the advanced model-based optimal control strategies, implemented in most process industries, are less common in WRRF. The complex nature of biological processes, the unavailability of simplified process models, and a lack of cost-effective surveillance infrastructure have often hindered the implementation of advanced control strategies in WRRF. The ambition of this research is to implement and validate cost-efficient monitoring alternatives and advanced control strategies for WRRF by fully utilizing the powerful Internet of Things (IoT) and data science tools. The first step towards implementing an advanced control strategy is to ensure the availability of surveillance infrastructure for monitoring nutrient compositions in WRRF processes. In Paper A, a soft sensor, based on Extended Kalman Filter, is developed for estimating water-quality parameters in a Sequential Batch MBBR process using reliable and inexpensive online sensors. The model used in the soft sensor combines the mechanistic understanding of the nutrient removal process with a statistical correlation between nutrient composition and easy-to-measure parameters. Paper B demonstrates the universality of the soft sensor through validation tests conducted in a Continuous Multistage MBBR pilot plant. The drift in soft-sensor estimation caused by a mismatch between the mathematical model and process behavior is studied in Paper B. The robustness of the soft sensor is assessed by observing estimated nutrient composition values for a period of three months. A systematic method to calibrate the measurement model and update model parameters using data from periodic lab measurements are discussed in Paper B. The term SCADA has been ubiquitous while mentioning online monitoring and control strategy deployment in WRRFs. The present digital world of affordable communication hardware, compact single board processors, and high computational power presents several options for remote monitoring and deployment of soft sensors. In Paper C, a cost-effective IoT strategy is developed by using an open-source programming language and inexpensive hardware. The functionalities of the IoT infrastructure are demonstrated by using it to deploy a soft sensor script in the ContinuousMultistage MBBR pilot plant. A cost-comparison between the commercially available alternatives presented in Paper A and the open-source IoT strategy in Paper B and Paper C highlights the benefits of the new monitoring infrastructure. Lack of reliable control models have often been the cause for the poor performance of advanced control strategies, such as Model Predictive Controls (MPC) when implemented to complex biological nutrient removal processes. Paper D attempts to overcome the inadequacies of the linear prediction model by combining a recursive model parameter estimator with the linear MPC. The new MPC variant, called the adaptive MPC (AMPC), reduces the dependency of MPC on the accuracy of its prediction model. The performance of the AMPC is compared with that of a linear MPC, nonlinear MPC, and the traditional proportional-integral cascade control through simulator-based evaluations conducted on the Benchmark Simulator platform(BSM2). The advantages of AMPC compared to its counterparts, in terms of reducing the aeration energy, curtailing the number of effluent ammonia violations, and the use of computational resources, are highlighted in Paper D. The complex interdependencies between different processes in a WRRF pose a significant challenge in defining constant reference points for WRRFs operations. A strategy that decides control outputs based on economic parameters rather than maintaining a fixed reference set-point is introduced in Paper E. The model-based control strategy presented in Paper D is further improved by including economic parameters in the MPC’s objective function. The control strategy known as Economic MPC (EMPC) is implemented for optimal dosage of magnesium hydroxide in a struvite recovery unit installed in a WRRF. A comparative study performed on the BSM2 platform demonstrates a significant improvement in overall profitability for the EMPC when compared to a constant or a feed-forward flow proportional control strategy. The resilience of the EMPC strategy to variations in the market price of struvite is also presented in Paper E. A combination of cost-effective monitoring infrastructure and advanced control strategies using advanced IoTs and data science tools have been documented to overcome some of the critical problems encountered in WRRFs. The overall improvement in process efficiency, reduction in operating costs, an increase in resource recovery, and a substantial reduction in the price of online monitoring infrastructure contribute to the overall aim of transitioning WRRFs to a self-sustaining facility capable of generating value-added products.Water Resource Recovery Facilities (WRRF), tidligere kjent som avløpsrenseanlegg (WWTP), blir stadig mer komplekse ettersom flere prosess steg tillegges anleggene i form av slambehandling og ressursgjenvinningsteknologi. Foruten hovedmålet om å imøtekomme strenge avløpsvannskvalitetskrav, har anleggenes fokus gradvis skiftet mot energieffektiv drift og gjenvinning av ressurser. Slike nye mål krever økonomisk optimal drift av prosesser som er utsatt for ekstreme variasjoner i volum og sammensetning av tilløp. Bruk av online overvåking, prosesskontroll og automatisering i WRRF har jevnt økt det siste tiåret. Likevel er avanserte modellbaserte kontrollstrategier for optimalisering ikke vanlig i WRRF, i motsetning til de fleste prosessindustrier. Komplekse forhold i biologiske prosesser, mangel på tilgang til pålitelige prosessmodeller og mangel på kostnadseffektiv overvåkingsinfrastruktur har ofte hindret implementeringen av avanserte kontrollstrategier i WRRF. Ambisjonen med denne avhandlingen er å implementere og validere kostnadseffektive overvåkingsalternativer og avanserte kontrollstrategier somutnytter kraftige Internet of Things (IoT) og datavitenskapelige verktøy i WRRF sammenheng. Det første steget mot implementering av en avansert kontrollstrategi er å sørge for tilgjengelighet av overvåkingsinfrastruktur for måling av næringsstoffer i WRRF-prosesser. Paper A demonstrerer en virtuell sensor basert på et utvidet Kalman filter, utviklet for å estimere vannkvalitetsparametere i en sekvensiell batch MBBR prosess ved hjelp av pålitelige og rimelige online sensorer. Modellen som brukes i den virtuelle sensoren kombinerer en mekanistisk forståelse av prosessen for fjerning av næringsstoffer fra avløpsvann med et statistisk sammenheng mellom næringsstoffsammensetning i avløpsvann og parametere som er enkle å måle. Paper B demonstrerer det universale bruksaspektet til den virtuelle sensoren gjennom valideringstester utført i et kontinuerlig flertrinns MBBR pilotanlegg. Feilene i sensorens estimering forårsaket av uoverensstemmelse mellom den matematiske modellen og prosesseatferden er undersøkt i Paper B. Robustheten til den virtuelle sensoren ble vurdert ved å observere estimerte næringssammensetningsverdier i en periode på tre måneder. En systematisk metode for å kalibrere målemodellen og oppdatere modellparametere ved hjelp av data fra periodiske laboratoriemålinger er diskutert i Paper B. Begrepet SCADA har alltid vært til stede når online overvåking og kontrollstrategi innen WRRF er nevnt. Den nåværende digitale verdenen med god tilgjengelighet av rimelig kommunikasjonsmaskinvare, kompakte enkeltkortprosessorer og høy beregningskraft presenterer flere muligheter for fjernovervåking og implementering av virtuelle sensorer. Paper C viser til utvikling av en kostnadseffektiv IoT-strategi ved hjelp av et programmeringsspråk med åpen kildekode og rimelig maskinvare. Funksjonalitetene i IoT-infrastruktur demonstreres gjennom implementering av et virtuelt sensorprogram i et kontinuerlig flertrinns MBBR pilotanlegg. En kostnadssammenligning mellom de kommersielt tilgjengelige alternativene som presenteres i Paper A og åpen kildekode-IoT-strategi i Paper B og Paper C fremhever fordelene med den nye overvåkingsinfrastrukturen. Mangel på pålitelige kontrollmodeller har ofte vært årsaken til svake resultater i avanserte kontrollstrategier, som for eksempel Model Predictive Control (MPC) når de implementeres i komplekse biologiske prosesser for fjerning av næringsstoffer. Paper D prøver å løse manglene i MPC ved å kombinere en rekursiv modellparameterestimator med lineær MPC. Den nye MPC-varianten, kalt Adaptiv MPC (AMPC), reduserer MPCs avhengighet av nøyaktigheten i prediksjonsmodellen. Ytelsen til AMPC sammenlignes med ytelsen til en lineær MPC, ikke-lineær MPC og tradisjonell proportionalintegral kaskadekontroll gjennom simulatorbaserte evalueringer utført på Benchmark Simulator plattformen (BSM2). Fordelene med AMPC sammenlignet med de andre kontrollstrategiene er fremhevet i Paper D og demonstreres i sammenheng redusering av energibruk ved lufting i luftebasseng, samt redusering i antall brudd på utslippskrav for ammoniakk og bruk av beregningsressurser. De komplekse avhengighetene mellom forskjellige prosesser i en WRRF utgjør en betydelig utfordring når man skal definere konstante referansepunkter for WRRF under drift. En strategi som bestemmer kontrollsignaler basert på økonomiske parametere i stedet for å opprettholde et fast referansesettpunkt introduseres i Paper E. Den modellbaserte kontrollstrategien fra PaperDforbedres ytterligere ved å inkludere økonomiske parametere iMPCs objektiv funksjon. Denne kontrollstrategien kalles Economic MPC (EMPC) og er implementert for optimal dosering av magnesiumhydroksid i en struvit utvinningsenhet installert i en WRRF. En sammenligningsstudie utført på BSM2 plattformen viste en betydelig forbedring i den totale lønnsomheten ved bruk av EMPC sammenlignet med en konstant eller en flow proportional kontrollstrategi. Robustheten til EMPC-strategien for variasjoner i markedsprisen på struvit er også demonstrert i Paper E. En kombinasjon av kostnadseffektiv overvåkingsinfrastruktur og avanserte kontrollstrategier ved hjelp av avansert IoT og datavitenskapelige verktøy er brukt for å løse flere kritiske utfordringer i WRRF. Den samlede forbedringen i prosesseffektivitet, reduksjon i operasjonskostnader, økt ressursgjenvinning og en betydelig reduksjon i pris for online overvåkningsinfrastruktur bidrar til det overordnede målet om å gå over til bærekraftige WRRF som er i stand til å generere verdiskapende produkter.DOSCON A

Closed-Loop Control of Anaesthetic Effect

Archivo disponible en la web de la revista, Open Access, en la siguiente URL: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anesthetic-effect Se puede referenciar de la siguiente manera: Santiago Torres, Juan A. Méndez, Héctor Reboso, José A. Reboso and Ana León (2012). Closed-Loop Control of Anaesthetic Effect, Pharmacology, Dr. Luca Gallelli (Ed.), InTech, DOI: 10.5772/37609. Available from: https://www.intechopen.com/books/pharmacology/closed-loop-control-of-anesthet

Nonlinear identification and control of muscle relaxant dynamics.

The work reported in this thesis comprised two major parts which are: 1) Off-line nonlinear identification of muscle relaxant dynamics, 2) Simulation-based design of a variety of controllers (ranging from classical PID to nonlinear self-tuners) for the closed-loop control of muscle relaxation. Relaxant drugs namely, Vecuronium and Atracurium are considered throughout. Off-line identification studies, using two special nonlinear identification packages (Nonlinear Identification package and Nonlinear Orthogonal Identification package), were carried out to determine nonlinear difference equation models (NARMAX) that best fit (in the least squares sense) recorded data from trials on humans and dogs for each drug. After validation, these models were assumed to represent, in a nonlinear polynomial form, the muscle relaxant drugs pharmacology. Two different approaches were explored for determining the physiological structure of both relaxant drugs: a) The drug model to comprise a pharmacokinetics part to represent the drug distribution, and pharmacodynamics which are often modelled by using the well known Hill equation. b) A cross-correlation approach based on Volterra series. With the relaxant dynamics structure thus fixed, the work proceeded to the control phase. Simple three-term PID controllers were first designed with their parameters being optimised, off-line, using the Simplex method. The non-adaptive nature of this class of controllers makes their robustness open to question when the system parameters for which they have been optimised change. Hence adaptive controllers in the form of linear and nonlinear generalised minimum variance, self-tuners, generalised predictive and nonlinear k-step ahead predictive controllers were also considered. All these latter control approaches are shown to be satisfactory, in terms of transient and steady state performance