Soft sensor development and process control of anaerobic digestion

This thesis focuses on soft sensor development based on fuzzy logic used for real time online monitoring of anaerobic digestion to improve methane output and for robust fermentation. Important process parameter indicators such as pH, biogas production, daily difference in pH and daily difference in biogas production were used to infer alkalinity, a reliable indicator of process stability. Additionally, a fuzzy logic and a rule-based controller were developed and tested with single stage anaerobic digesters operating with cow slurry and cellulose. Alkalinity predictions from the fuzzy logic algorithm were used by both controllers to regulate the organic loading rate that aimed to optimise the biogas process. The predictive performance of a software sensor determining alkalinity that was designed using fuzzy logic and subtractive clustering and was validated against multiple linear regression models that were developed (Partner N° 2, Rothamsted Research 2010) for the same purpose. More accurate alkalinity predictions were achieved by utilizing a fuzzy software sensor designed with less amount of data compared to a multiple linear regression model whose design was based on a larger database. Those models were utilised to control the organic loading rate of a twostage, semi-continuously fed stirred reactor system. Three 5l reactors without support media and three 5l reactors with different support media (burst cell reticulated polyurethane foam coarse, burst cell reticulated polyurethane foam medium and sponge) were operated with cow slurry for a period of seven weeks and twenty weeks respectively. Reactors with support media were proven to be more stable than the reactors without support media but did not exhibit higher gas productivity. Biomass support media were found to influence digester recovery positively by reducing the recovery period. Optimum process parameter ranges were identified for reactors with and without support media. Increased biogas production was found to occur when the loading rates were 3-3.5g VS/l/d and 4-5g VS/l/d respectively. Optimum pH ranges were identified between 7.1-7.3 and 6.9-7.2 for reactors with and without support media respectively, whereas all reactors became unstable at ph<6.9. Alkalinity levels for system stability appeared to be above 3500 mg/l of HCO3 - for reactors without media and 3480 mg/l of HCO3 - for reactors with support media. Biogas production was maximized when alkalinity was 3 between 3500-4500 mg/l of HCO3 - for reactors without support media and 3480- 4300 mg/l of HCO3 - for reactors with support media. Two fuzzy logic models predicting alkalinity based on the operation of the three 5l reactors with support media were developed (FIS I, FIS II). The FIS II design was based on a larger database than FIS I. FIS II performance when applied to the reactor where sponge was used as the support media was characterized by quite good MAE and bias values of 466.53 mg/l of HCO3- and an acceptable value for R2= 0.498. The NMSE was close to 0 with a value of 0.03 and a slightly higher FB= 0.154 than desired. The fuzzy system robustness was tested by adding NaHCO3 to the reactor with the burst cell reticulated polyurethane foam medium and by diluting the reactor where sponge was used as the support media with water. FIS I and FIS II were able to follow the system output closely in the first case, but not in the second. FIS II functionality as an alkalinity predictor was tested through the application on a 28l cylindrical reactor with sponge as the biomass support media treating cow manure. If data that was recorded when severe temperature fluctuations occurred (that highly impact digester performance), are excluded, FIS II performance can be characterized as good by having R2= 0.54 and MAE=Bias= 587 mg/l of HCO3-. Predicted alkalinity values followed observed alkalinity values closely during the days that followed NaHCO3 addition and water dilution. In a second experiment a rulebased and a Mamdani fuzzy logic controller were developed to regulate the organic loading rate based on alkalinity predictions from FIS II. They were tested through the operation of five 6.5l reactors with biomass support media treating cellulose. The performance indices of MAE=763.57 mg/l of HCO3-, Bias= 398.39 mg/l of HCO3-, R2= 0.38 and IA= 0.73 indicate a pretty good correlation between predicted and observed values. However, although both controllers managed to keep alkalinity within the desired levels suggested for stability (>3480 mg/l of HCO3-), the reactors did not reach a stable state suggesting that different loading rates should be applied for biogas systems treating cellulose.New Generation Biogas (NGB

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Métodos clásicos y de soft-computing en la optimización de procesos complejos: Aplicación a un proceso de fabricación

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, febrero de 201

Design and implementation of a modular controller for robotic machines

This research focused on the design and implementation of an Intelligent Modular Controller (IMC) architecture designed to be reconfigurable over a robust network. The design incorporates novel communication, hardware, and software architectures. This was motivated by current industrial needs for distributed control systems due to growing demand for less complexity, more processing power, flexibility, and greater fault tolerance. To this end, three main contributions were made. Most distributed control architectures depend on multi-tier heterogeneous communication networks requiring linking devices and/or complex middleware. In this study, first, a communication architecture was proposed and implemented with a homogenous network employing the ubiquitous Ethernet for both real-time and non real-time communication. This was achieved by a producer-consumer coordination model for real-time data communication over a segmented network, and a client-server model for point-to-point transactions. The protocols deployed use a Time-Triggered (TT) approach to schedule real-time tasks on the network. Unlike other TT approaches, the scheduling mechanism does not need to be configured explicitly when controller nodes are added or removed. An implicit clock synchronization technique was also developed to complement the architecture. Second, a reconfigurable mechanism based on an auto-configuration protocol was developed. Modules on the network use this protocol to automatically detect themselves, establish communication, and negotiate for a desired configuration. Third, the research demonstrated hardware/software co-design as a contribution to the growing discipline of mechatronics. The IMC consists of a motion controller board designed and prototyped in-house, and a Java microcontroller. An IMC is mapped to each machine/robot axis, and an additional IMC can be configured to serve as a real-time coordinator. The entire architecture was implemented in Java, thus reinforcing uniformity, simplicity, modularity, and openness. Evaluation results showed the potential of the flexible controller to meet medium to high performance machining requirements

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Process Control of Crushing Circuits

Kivenmurskaus on keskeinen osaprosessi kiviaineksen, metallien ja sementin tuotannossa. Murskaamalla tuotetut raaka-aineet muodostavat nykyaikaisen infrastruktuurimme perustan. Huolimatta merkittävästä roolistaan, kivenmurskaus on yksi harvoista teollisista prosesseista, jonka prosessinohjaus toteutetaan edelleen kokemusperäisesti, ilman luotettavaa mittaustietoa suoritettujen ohjaustoimien vaikutuksista. Nykykäytäntö altistaa murskausprosessit prosessivaihteluille ja –häiriöille, ja johtaa viime kädessä tehottomaan tuotantoon ja kapasiteetin vajaakäyttöön. Pääsyinä nykytilaan voidaan pitää murskausprosessien puutteellista anturointia ja tutkimustiedon puutetta korkeamman automaatioasteen tuomista hyödyistä. Tässä väitöskirjassa pyrittiin ratkaisemaan edellä mainittu ongelma automaattisen prosessinohjauksen avulla. Päätavoitteena oli kehittää säätömenetelmät murskauspiirin suorituskyvyn saattamiseksi lähelle parasta saavutettavissa olevaa tasoa. Tämä tutkimus perustuu mallipohjaiseen säädönsuunnittelumenetelmään. Systemaattinen suunnitteluprosessi alkoi säätötavoitteiden määrittelystä ja dynaamisten prosessimallien kehittämisestä. Kehitettyjen prosessimallien avulla luotiin säätötavoitteet täyttävä säätöstrategia ja viritettiin strategian vaatimat prosessisäätimet. Lopuksi simulointimallien avulla kehitetty ja testattu säätöstrategia implementoitiin osaksi laitoksen automaatiojärjestelmää ja sen suorituskyky arvioitiin täyden mittakaavan prosessikokeiden avulla. Tämä väitöskirja on osoittanut, että murskauspiirin tehokas ja tarkoituksenmukainen toiminta vaatii eri kahden säätötavan toteuttamista: massataseen säätö ja hienonnusmäärän säätö. Massatasesäädön tavoitteena on varmistaa 100 % käyttöaste murskauspiirin pullonkaulassa. Hienonnusmäärän säätö varmistaa halutun murskaimen tuotemateriaalin partikkelikokojakauman. Kehitetyt hienonnusmäärän säätömenetelmät perustuvat itseoptimoituvaan säätötapaan, joka mahdollistaa likimain optimaalisen suorituskyvyn käyttämällä säätimessä vakio-asetusarvoa. Kun tämä asetusarvo valitaan optimaalisesti, mahdollistaa esitelty ohjausstrategia parhaan saavutettavissa olevan murskauspiirin suorituskyvyn. Työn merkittävä tunnuspiirre on erityisen kattava empiria. Kehitetyt menetelmät testattiin kattavasti useissa erilaisissa tuotantoskenaarioissa ja prosessikonfiguraatioissa. Täyden mittakaavan prosessikokeiden tulokset vastasivat hyvin lähelle simulaatioilla saatuja tuloksia. Tämä väitöskirja on merkittävä edistysaskel murskausprosessien säädössä. Työn tuloksena kehitetyt mittaus- ja säätötavat mahdollistavat tehokkaamman ja tarkoituksenmukaisemman raaka-ainetuotannon. Työn tuloksilla voidaan olettaa olevan merkittävä vaikutus siihen, miten ja millä tavoin murskausprosesseja ohjataan tulevaisuudessa. Työssä kehitetyn murskauspiirin automaattisen säätöstrategian voidaan olettaa toimivan perustana tulevaisuuden murskausprosessien prosessiautomaatio-toteutuksille.Crushing is an essential high-volume processing stage in the production of aggregates, metals and cement. Crushed products form the basis of our modern infrastructure and therefore play a major role in the economic growth and welfare. Despite its significant role in society, crushing is one of the few remaining industrial processes that is currently being operated using belief-based manual control without the possibility to quantify the consequences of performed control actions. This practice makes crushing processes vulnerable to process variation and exposes them to inefficient production and capacity underutilization. The aim of this thesis is to address this deficiency by bridging the gap between theoretically possible and realized crushing circuit performance, by means of automatic process control. This thesis covers the entire model-based control system design procedure – from the formulation of control objectives and development of dynamic process model(s), through the development of control strategy, to the control system implementation and performance evaluation – for crushing circuits. Research has led to significant advances within crushing process measurement and control. Developed methods have been rigorously tested in various production scenarios and circuit flowsheets, using both dynamic simulations and full-scale experiments. Experiments revealed expected behavior with a significant increase in performance. The results have shown that the efficient operation of a crushing circuit requires addressing two control tasks: mass balance control and size reduction control. The objective of mass balance control is to guarantee 100 percent circuit utilization, whereas size reduction control ensures the desired degree of size reduction. The ideal degree of size reduction is determined empirically to maximize the value of the used KPI. The developed control strategy delivers near-maximum circuit performance. This thesis represents a major leap forward in the area of process control of crushing circuits. It has opened entirely new possibilities by making it possible to quantify the instantaneous performance of crushing circuits and by introducing the ability to ensure consistent and efficient long-term production. These major breakthroughs can have a significant impact on how crushing plants will be operated in the future. Developed standard control practice can be expected to serve as a basis for future control system implementations of industrial crushing circuits

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research