Issues Faced in a Remote Instrumentation Laboratory

An Online Lab is a multi-university shared laboratory environment, where students can exercise their knowledge as they would do in a physical lab. The idea is to have maximum resource utilization and collaboration between universities by sharing of ideas. This kind of remote laboratory negates the economic issues to set up a laboratory and allows every student to have an experience of real laboratory. As part of Ministry of Human Resource Development (MHRD) Robotics Lab project a study on state of art of remote labs was conducted. This paper discusses some key issues in the design and operation of such remote labs. The lab should be remotely usable by a large student body, with varied levels of sophistication, all the way from elementary learners, to PhD students doing research. In addition, the high design load implies that the architecture should be highly parallel, and structurally reliable

The OpenModelica integrated environment for modeling, simulation, and model-based development

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica- UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.Fil: Fritzson, Peter. Linköping University; SueciaFil: Pop, Adrian. Linköping University; SueciaFil: Abdelhak, Karim. Fachhochschule Bielefeld; AlemaniaFil: Asghar, Adeel. Linköping University; SueciaFil: Bachmann, Bernhard. Fachhochschule Bielefeld; AlemaniaFil: Braun, Willi. Fachhochschule Bielefeld; AlemaniaFil: Bouskela, Daniel. Electricité de France; FranciaFil: Braun, Robert. Linköping University; SueciaFil: Buffoni, Lena. Linköping University; SueciaFil: Casella, Francesco. Politecnico di Milano; ItaliaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Franke, Rüdiger. Abb Group; AlemaniaFil: Fritzson, Dag. Linköping University; SueciaFil: Gebremedhin, Mahder. Linköping University; SueciaFil: Heuermann, Andreas. Linköping University; SueciaFil: Lie, Bernt. University of South-Eastern Norway; NoruegaFil: Mengist, Alachew. Linköping University; SueciaFil: Mikelsons, Lars. Linköping University; SueciaFil: Moudgalya, Kannan. Indian Institute Of Technology Bombay; IndiaFil: Ochel, Lennart. Linköping University; SueciaFil: Palanisamy, Arunkumar. Linköping University; SueciaFil: Ruge, Vitalij. Fachhochschule Bielefeld; AlemaniaFil: Schamai, Wladimir. Danfoss Power Solutions GmbH & Co; AlemaniaFil: Sjolund, Martin. Linköping University; SueciaFil: Thiele, Bernhard. Linköping University; SueciaFil: Tinnerholm, John. Linköping University; SueciaFil: Ostlund, Per. Linköping University; Sueci

EFFECTIVE DIFFUSIVITIES IN CHAR PARTICLES (DIFFUSION, FAST FOURIER TRANSFORM, PYROLYSIS, COAL, FLUIDIZED BED)

Illinois #6, Illinois #5, Pittsburgh #8 coal and Texas Lignite particles of size range 150-16 mesh were pyrolyzed at a heating rate of 3.8 K/min up to 900(DEGREES)C. A stainless steel annular fluidized bed reactor yielded unagglomerated char particles. On-line analysis of the low molecular weight gaseous effluents was performed using a Series-Bypass column arrangement. The effluent profiles were in agreement with previous results. The char particles were gasified with oxygen in the annular fluidized bed reactor and with CO(,2) in a U tube spouted bed reactor. In both cases, the on-line analysis of the effluent gases helped determine the conversion of char. The transient pulse chromatography method was chosen to measure the macro pore effective diffusivities in Illinois #6 and Texas Lignite char particles reacted with oxygen. For a known pulse of helium into a Single Pellet String Reactor packed with char particles, the output chromatogram was measured as discrete data. Using the Kubin-Kucera model, the output profile was predicted also in discrete form in the Fourier domain. The residuals, the (,2) norms of the differences between the model predictions and the experimental observations, were minimized to extract the model parameters. The COMPLEX optimization procedure and an intelligent search scheme were used for this purpose. This is the first time the Discrete and Fast Fourier Transform techniques have been used to extract the model parameters. The intelligent search scheme is another novelty of this work. The diffusivities increased linearly with porosity for the reacted Illinois #6 and Texas lignite char. The tortuosity factor was found to be about 75 for the reacted Illinois #6 char and about 175 for Texas Lignite char. It was not possible to determine the diffusivity values for the unreacted Illinois char

Strategies to improve collaboration in a problem-based learning environment : Alien Rescue

There is an ever growing need to research and develop pedagogical strategies that cater to the 21st-century skills. Problem-Based Learning (PBL) is one such instructional strategy. There are currently many PBL environments, but for this report, I use a PBL environment called ‘Alien Rescue’ as an example. PBL has many goals that it wishes to achieve. In this report, I highlight the importance of five such goals. I then review the practices in Alien Rescue to see which goals it supports the best and which goal could need improvements. I then choose to focus on one of the very important goals, namely, collaboration. I again review the literature to find strategies that support collaborative learning in PBL. Finally, I contextualize these strategies to Alien Rescue. I make specific suggestions that will improve collaboration, offer implementation tactics for these suggestions, and also give ways to evaluate the effectiveness of the suggestions in Alien Rescue. I hope to help in the research and development process of Alien Rescue with this report.Curriculum and Instructio

Prioritized Model Predictive Control for Quadruple Tank System

by A. T. Markana, Nitin Padhiyar and K. Moudgaly

Multi-criterion control of a bioprocess in fed-batch reactor using EKF based economic model predictive control

This research paper presents an offline and online user defined priority driven multi-objective optimal control study of a bioprocess in a fed-batch reactor. Productivity and the amount of substrates used in the process are considered as the two control objectives in that order of priority for this purpose. The priorities in the objective functions are realized using the lexicographic approach by sequentially solving multiple objectives to arrive at a Pareto solution point. This approach is not sensitive to the tuning of weighting parameters as compared to the scalarized objective, practiced conventionally. The weighting factors tuning issue is demonstrated with an offline optimal control. The lexicographic optimization approach is then implemented to overcome this thing issue. Subsequently, the online optimal control problem is solved using economic model predictive control (EMPC) owing to the economic nature of the control objectives. Often, the Pareto curve is such that marginally relaxing one objective results into a significant improvement in the other objective. This can easily be implemented with the lexicographic approach and is demonstrated using EMPC. Moreover, unlike the continuous processes, the batch processes operate for a specific batch time. Hence, the shrinking horizon approach along with the EMPC framework is employed in the fed-batch bioreactor for online control with extended Kalman filter (EKF).by Markana Anilkumar, Nitin Padhiyar and Kannan Moudgaly

Lexicographic optimization based MPC: simulation and experimental study

Multi-variable prioritized control study is carried out using model predictive control (MPC) algorithms. The conventional MPC algorithm implements multi-variable control through one augmented objective function and requires weights adjustment for required performance. In order to implement explicit prioritization in multiple control objectives, we have used lexicographic MPC. To achieve better tracking performance, we have used a new MPC algorithm, by modifying the lexicographic constraint, referred to as MLMPC, where tuning of weights is not required. The effectiveness of MLMPC algorithm is demonstrated on a PMMA reactor for controlling the number average molecular weight and the reactor temperature. We have also verified the benefits of proposed algorithm on an experimental single board heater system (SBHS) for controlling temperature of a thin metal plate. These simulation and experimental studies demonstrate the superiority of the proposed method over conventional MPC and lexicographic MPC. Finally, we have presented generalized mathematical solutions to the optimization problem in MLMPCby Nitin Padhiyarby Markana Anilkumar, Nitin Padhiyar and Kannan Moudgalya

Prioritized control of multivariate process using lexicographic ordering approach: a simulation study

Design of a multivariate control system is a challenging task owing to inherent process nonlinearities, multivariable interactions, and unstable zeros in the process dynamics. Prioritization of multiple control objectives is conventionally achieved using scalarization approach by appropriate weighting factors in the augmented objective function. However, the optimality of these parameters does not hold in presence of unmeasured disturbance and different setpoints. Hence, prioritization in the objectives is not achieved in such circumstances. On the other hand, priority in various objectives can effectively be realized in lexicographic optimization approach. We use the lexicographic optimization approach to address setpoint tracking control problem in linear MPC. To validate the efficacy of the lexicographic ordering approach, multivariate quadruple tank process is considered. Tuning issues with conventional MPC and its remedies using lexicographic approach are discussed in the present work.by Markana Anilkumar, Nitin Padhiyar and Kannan Moudgaly