Monitoramento por microcontrolador do cultivo de spirulina em fotobiorreator raceway

A oportunidade de produção de biomassa microalgal tem despertado interesse pelos diversos destinos que a mesma pode ter, seja na produção de bioenergia, como fonte de alimento ou servindo como produto da biofixação de dióxido de carbono. Em geral, a produção em larga escala de cianobactérias e microalgas é feita com acompanhamento através de análises físicoquímicas offline. Neste contexto, o objetivo deste trabalho foi monitorar a concentração celular em fotobiorreator raceway para produção de biomassa microalgal usando técnicas de aquisição digital de dados e controle de processos, pela aquisição de dados inline de iluminância, concentração de biomassa, temperatura e pH. Para tal fim foi necessário construir sensor baseado em software capaz de determinar a concentração de biomassa microalgal a partir de medidas ópticas de intensidade de radiação monocromática espalhada e desenvolver modelo matemático para a produção da biomassa microalgal no microcontrolador, utilizando algoritmo de computação natural no ajuste do modelo. Foi projetado, construído e testado durante cultivos de Spirulina sp. LEB 18, em escala piloto outdoor, um sistema autônomo de registro de informações advindas do cultivo. Foi testado um sensor de concentração de biomassa baseado na medição da radiação passante. Em uma segunda etapa foi concebido, construído e testado um sensor óptico de concentração de biomassa de Spirulina sp. LEB 18 baseado na medição da intensidade da radiação que sofre espalhamento pela suspensão da cianobactéria, em experimento no laboratório, sob condições controladas de luminosidade, temperatura e fluxo de suspensão de biomassa. A partir das medidas de espalhamento da radiação luminosa, foi construído um sistema de inferência neurofuzzy, que serve como um sensor por software da concentração de biomassa em cultivo. Por fim, a partir das concentrações de biomassa de cultivo, ao longo do tempo, foi prospectado o uso da plataforma Arduino na modelagem empírica da cinética de crescimento, usando a Equação de Verhulst. As medidas realizadas no sensor óptico baseado na medida da intensidade da radiação monocromática passante através da suspensão, usado em condições outdoor, apresentaram baixa correlação entre a concentração de biomassa e a radiação, mesmo para concentrações abaixo de 0,6 g/L. Quando da investigação do espalhamento óptico pela suspensão do cultivo, para os ângulos de 45º e 90º a radiação monocromática em 530 nm apresentou um comportamento linear crescente com a concentração, apresentando coeficiente de determinação, nos dois casos, 0,95. Foi possível construir um sensor de concentração de biomassa baseado em software, usando as informações combinadas de intensidade de radiação espalhada nos ângulos de 45º e 135º com coeficiente de determinação de 0,99. É factível realizar simultaneamente a determinação inline de variáveis do processo de cultivo de Spirulina e a modelagem cinética empírica do crescimento do micro-organismo através da equação de Verhulst, em microcontrolador Arduino.

Thermal Tracking and Estimation for Microprocessors

Due to increasing integration density and operating frequency of today's high performance processors, the temperature of a typical chip can easily exceed 100 degrees Celsius. However, the runtime thermal state of a chip is very hard to predict and manage due to the random nature in computing workloads, as well as the process, voltage and ambient temperature variability (together called PVT variability). The uneven nature (both in time and space) of the heat dissipation of the chip could lead to severe reliability issues and error-prone chip behavior (e.g. timing errors). Many dynamic power/thermal management techniques have been proposed to address this issue such as dynamic voltage and frequency scaling (DVFS), clock gating and etc. However, most of such techniques require accurate knowledge of the runtime thermal state of the chip to make efficient and effective control decisions. In this work we address the problem of tracking and managing the temperature of microprocessors which include the following sub-problems: (1) how to design an efficient sensor-based thermal tracking system on a given design that could provide accurate real-time temperature feedback; (2) what statistical techniques could be used to estimate the full-chip thermal profile based on very limited (and possibly noise-corrupted) sensor observations; (3) how do we adapt to changes in the underlying system's behavior, since such changes could impact the accuracy of our thermal estimation. The thermal tracking methodology proposed in this work is enabled by on-chip sensors which are already implemented in many modern processors. We first investigate the underlying relationship between heat distribution and power consumption, then we introduce an accurate thermal model for the chip system. Based on this model, we characterize the temperature correlation that exists among different chip modules and explore statistical approaches (such as those based on Kalman filter) that could utilize such correlation to estimate the accurate chip-level thermal profiles in real time. Such estimation is performed based on limited sensor information because sensors are usually resource constrained and noise-corrupted. We also took a further step to extend the standard Kalman filter approach to account for (1) nonlinear effects such as leakage-temperature interdependency and (2) varying statistical characteristics in the underlying system model. The proposed thermal tracking infrastructure and estimation algorithms could consistently generate accurate thermal estimates even when the system is switching among workloads that have very distinct characteristics. Through experiments, our approaches have demonstrated promising results with much higher accuracy compared to existing approaches. Such results can be used to ensure thermal reliability and improve the effectiveness of dynamic thermal management techniques.

Idiotypic Immune Networks in Mobile Robot Control

Jerne's idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

Mid-infrared photonic sensors based on metamaterial structures

In this work three different metallic metamaterials (MMs) structures such as asymmetric split ring resonators (A-SRRs), dipole and split H-shaped (ASHs) structures that support plasmonic resonances have been developed. The aim of the work involves the optimization of photonic sensor based on plasmonic resonances and surface enhanced infrared absorption (SEIRA) from the MM structures. The MMs structures were designed to tune their plasmonic resonance peaks in the mid-infrared region. The plasmonic resonance peaks produced are highly dependent on the structural dimension and polarisation of the electromagnetic (EM) source. The ASH structure particularly has the ability to produce the plasmonic resonance peak with dual polarisation of the EM source. The double resonance peaks produced due to the asymmetric nature of the structures were optimized by varying the fundamental parameters of the design. These peaks occur due to hybridization of the individual elements of the MMs structure. The presence of a dip known as a trapped mode in between the double plasmonic peaks helps to narrow the resonances. A periodicity greater than twice the length and diameter of the metallic structure was applied to produce narrow resonances for the designed MMs. A nanoscale gap in each structure that broadens the trapped mode to narrow the plasmonic resonances was also used. A thickness of 100 nm gold was used to experimentally produce a high quality factor of 18 in the mid-infrared region. The optimised plasmonic resonance peaks was used for detection of an analyte, 17β-estradiol. 17β-estradiol is mostly responsible for the development of human sex organs and can be found naturally in the environment through human excreta. SEIRA was the method applied to the analysis of the analyte. The work is important in the monitoring of human biology and in water treatment. Applying this method to the developed nano-engineered structures, enhancement factors of 10^5 and a sensitivity of 2791 nm/RIU was obtained. With this high sensitivity a figure of merit (FOM) of 9 was also achieved from the sensors. The experiments were verified using numerical simulations where the vibrational resonances of the C-H stretch from 17β-estradiol were modelled. Lastly, A-SRRs and ASH on waveguides were also designed and evaluated. These patterns are to be use as basis for future work.

Idiotypic Immune Networks in Mobile Robot Control

Jerne's idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks

In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

MEMS SENSOR PLATFORMS FOR IN SITU CHARACTERIZATION OF LI-ION BATTERY ELECTRODES

Lithium-ion batteries provide high energy density while being compact and light-weight and are the most pervasive energy storage technology powering portable electronic devices such as smartphones, laptops, and tablet PCs. Considerable efforts have been made to develop new electrode materials with ever higher capacity, while being able to maintain long cycle life. A key challenge in those efforts has been characterizing and understanding these materials during battery operation. While it is generally accepted that the repeated strain/stress cycles play a role in long-term battery degradation, the detailed mechanisms creating these mechanical effects and the damage they create still remain unclear. Therefore, development of techniques which are capable of capturing in real time the microstructural changes and the associated stress during operation are crucial for unravelling lithium-ion battery degradation mechanisms and further improving lithium-ion battery performance. This dissertation presents the development of two microelectromechanical systems sensor platforms for in situ characterization of stress and microstructural changes in thin film lithium-ion battery electrodes, which can be leveraged as a characterization platform for advancing battery performance. First, a Fabry-Perot microelectromechanical systems sensor based in situ characterization platform is developed which allows simultaneous measurement of microstructural changes using Raman spectroscopy in parallel with qualitative stress changes via optical interferometry. Evolutions in the microstructure creating a Raman shift from 145 cm−1 to 154 cm−1 and stress in the various crystal phases in the LixV2O5 system are observed, including both reversible and irreversible phase transitions. Also, a unique way of controlling electrochemically-driven stress and stress gradient in lithium-ion battery electrodes is demonstrated using the Fabry-Perot microelectromechanical systems sensor integrated with an optical measurement setup. By stacking alternately stressed layers, the average stress in the stacked electrode is greatly reduced by 75% compared to an unmodified electrode. After 2,000 discharge-charge cycles, the stacked electrodes retain only 83% of their maximum capacity while unmodified electrodes retain 91%, illuminating the importance of the stress gradient within the electrode. Second, a buckled membrane microelectromechanical systems sensor is developed to enable in situ characterization of quantitative stress and microstructure evolutions in a V2O5 lithium-ion battery cathode by integrating atomic force microscopy and Raman spectroscopy. Using dual-mode measurements in the voltage range of the voltage range of 2.8V – 3.5V, both the induced stress (~ 40 MPa) and Raman intensity changes due to lithium cycling are observed. Upon lithium insertion, tensile stress in the V2O5 increases gradually until the α- to ε-phase and ε- to δ-phase transitions occur. The Raman intensity change at 148 cm−1 shows that the level of disorder increases during lithium insertion and progressively recovers the V2O5 lattice during lithium extraction. Results are in good agreement with the expected mechanical behavior and disorder change in V2O5, highlighting the potential of microelectromechanical systems as enabling tools for advanced scientific investigations. The work presented here will be eventually utilized for optimization of thin film battery electrode performance by achieving fundamental understanding of how stress and microstructural changes are correlated, which will also provide valuable insight into a battery performance degradation mechanism.

Properties of polycrystalline gas sensors based on d.c. and a.c. electrical measurements

Electrical properties of polycrystalline gas sensors are analyzed by d.c. and a.c. measurements. d.c. electrical conductivity values compared with those obtained by admittance spectroscopy methods help to obtain a detailed 'on line' analysis of conductivity-modulated gas sensors. The electrical behaviour of grain boundaries is obtained and a new design of sensors can be achieved by enhancing the activity of surface states in the detecting operation. A Schottky barrier model is used to explain the grain boundary action under the presence of surrounding gases. The height of this barrier is a function of gas concentration due to the trapping of excess charge generated by gas adsorption at the interface. A comparison between this dependence, and a plot of the real and imaginary components of the admittance versus frequency at different gas concentrations, provides information on the different parameters that play a role in the conduction mechanisms. These methods have been applied to the design of a CO sensor based on tin oxide films for domestic purposes, the characteristics of which are presented.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Slip ratio in dispersed viscous oil-water pipe flow

In this article, dispersed flow of viscous oil and water is investigated. The experimental work was performed in a 26.2-mm-i.d. 12-m-long horizontal glass pipe using water and oil (viscosity of 100 mPa s and density of 860 kg/m(3)) as test fluids. High-speed video recording and a new wire-mesh sensor based on capacitance (permittivity) measurements were used to characterize the flow. Furthermore, holdup data were obtained using quick-closing-valves technique (QCV). An interesting finding was the oil-water slip ratio greater than one for dispersed flow at high Reynolds number. Chordal phase fraction distribution diagrams and images of the holdup distribution over the pipe cross-section obtained via wire-mesh sensor indicated a significant amount of water near to the pipe wall for the three different dispersed flow patterns identified in this study: oil-in-water homogeneous dispersion (o/w H), oil-in-water non-homogeneous dispersion (o/w NH) and Dual continuous (Do/w & Dw/o). The phase slip might be explained by the existence of a water film surrounding the homogeneous mixture of oil-in-water in a hidrofilic-oilfobic pipe. (C) 2010 Elsevier Inc. All rights reserved.

Infinite horizon MPC with non-minimal state space feedback

In the MPC literature, stability is usually assured under the assumption that the state is measured. Since the closed-loop system may be nonlinear because of the constraints, it is not possible to apply the separation principle to prove global stability for the Output feedback case. It is well known that, a nonlinear closed-loop system with the state estimated via an exponentially converging observer combined with a state feedback controller can be unstable even when the controller is stable. One alternative to overcome the state estimation problem is to adopt a non-minimal state space model, in which the states are represented by measured past inputs and outputs [P.C. Young, M.A. Behzadi, C.L. Wang, A. Chotai, Direct digital and adaptative control by input-output, state variable feedback pole assignment, International journal of Control 46 (1987) 1867-1881; C. Wang, P.C. Young, Direct digital control by input-output, state variable feedback: theoretical background, International journal of Control 47 (1988) 97-109]. In this case, no observer is needed since the state variables can be directly measured. However, an important disadvantage of this approach is that the realigned model is not of minimal order, which makes the infinite horizon approach to obtain nominal stability difficult to apply. Here, we propose a method to properly formulate an infinite horizon MPC based on the output-realigned model, which avoids the use of an observer and guarantees the closed loop stability. The simulation results show that, besides providing closed-loop stability for systems with integrating and stable modes, the proposed controller may have a better performance than those MPC controllers that make use of an observer to estimate the current states. (C) 2008 Elsevier Ltd. All rights reserved.

A dynamic model with on-line identification for rotary sugar drying processes

A dynamic modelling methodology, which combines on-line variable estimation and parameter identification with physical laws to form an adaptive model for rotary sugar drying processes, is developed in this paper. In contrast to the conventional rate-based models using empirical transfer coefficients, the heat and mass transfer rates are estimated by using on-line measurements in the new model. Furthermore, a set of improved sectional solid transport equations with localized parameters is developed in this work to reidentified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.place the global correlation for the computation of solid retention time. Since a number of key model variables and parameters are identified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.

Engineering real-time applications with WorldFIP: tools and analysis

WorldFIP is standardised as European Norm EN 50170 - General Purpose Field Communication System. Field communication systems (fieldbuses) started to be widely used as the communication support for distributed computer-controlled systems (DCCS), and are being used in all sorts of process control and manufacturing applications within different types of industries. There are several advantages in using fieldbuses as a replacement of for the traditional point-to-point links between sensors/actuators and computer-based control systems. Indeed they concern economical ones (cable savings) but, importantly, fieldbuses allow an increased decentralisation and distribution of the processing power over the field. Typically DCCS have real-time requirements that must be fulfilled. By this, we mean that process data must be transferred between network computing nodes within a maximum admissible time span. WorldFIP has very interesting mechanisms to schedule data transfers. It explicit distinguishes to types of traffic: periodic and aperiodic. In this paper we describe how WorldFIP handles these two types of traffic, and more importantly, we provide a comprehensive analysis for guaranteeing the real-time requirements of both types of traffic. A major contribution is made in the analysis of worst-case response time of aperiodic transfer requests.

Distributed computing for the factory-floor: a real-time approach using WorldFIP networks

Field communication systems (fieldbuses) are widely used as the communication support for distributed computer-controlled systems (DCCS) within all sort of process control and manufacturing applications. There are several advantages in the use of fieldbuses as a replacement for the traditional point-to-point links between sensors/actuators and computer-based control systems, within which the most relevant is the decentralisation and distribution of the processing power over the field. A widely used fieldbus is the WorldFIP, which is normalised as European standard EN 50170. Using WorldFIP to support DCCS, an important issue is “how to guarantee the timing requirements of the real-time traffic?” WorldFIP has very interesting mechanisms to schedule data transfers, since it explicitly distinguishes periodic and aperiodic traffic. In this paper, we describe how WorldFIP handles these two types of traffic, and more importantly, we provide a comprehensive analysis on how to guarantee the timing requirements of the real-time traffic.

Towards Active Course Marks for Autonomous Sailing Competitions

This paper describes the environmental monitoring / regatta beacon buoy under development at the Laboratory of Autonomous Systems (LSA) of the Polytechnic Institute of Porto. On the one hand, environmentalmonitoring of open water bodies in real or deferred time is essential to assess and make sensible decisions and, on the other hand, the broadcast in real time of position, water and wind related parameters allows autonomous boats to optimise their regatta performance. This proposal, rather than restraining the boats autonomy, fosters the development of intelligent behaviour by allowing the boats to focus on regatta strategy and tactics. The Nautical and Telemetric Application (NAUTA) buoy is a dual mode reconfigurable system that includes communications, control, data logging, sensing, storage and power subsystems. In environmental monitoring mode, the buoy gathers and stores data from several underwater and above water sensors and, in regatta mode, the buoy becomes an active course mark for the autonomous sailing boats in the vicinity. During a race, the buoy broadcasts its position, together with the wind and the water current local conditions, allowing autonomous boats to navigate towards and round the mark successfully. This project started with the specification of the requirements of the dual mode operation, followed by the design and building of the buoy structure. The research is currently focussed on the development of the modular, reconfigurable, open source-based control system. The NAUTA buoy is innovative, extensible and optimises the on board platform resources.