Automation of a Distillation Column of Packed Bed for an Alcohol Solution using Arduino

This study demonstrates the use of Arduino to automate the operation of a distillation column binary ethanol - water as learning material used by students in the career of Chemical Engineering at the Escuela Superior Politecnica del Litoral. The main objective was to build an automated computer with the technology of arduino, in which thermocouples were used to offer students the convenience of taking temperature readings on a LCD display and in turn it allowed us to observe the level of the solution to control ignition resistance. So they were able to identify the processes that occur during the distillation unit operation automatically and manually in the Unit Operations Laboratory

Development of soft computing tools and IoT for improving the performance assessment of analysers in a clinical laboratory

This paper presents a three phase methodology to automate quality control in healthcare clinical laboratory. The first phase consists in the automation of the performance assessment of the equipment in MS Excel. With the smart tools included in Excel, a macro was developed that not only saves the user time and makes the process more efficient, but also gives a clear idea of the quality of the test results. The second phase deals with the quality control management of the generated data through the application of manufacturing techniques; a code in Matlab was created that would allow the user to visualise the current performance of the equipment according to some specified limits in Statistical Process Control (SPC) charts. This enables the user to select the relevant information to visualise by analysing the control levels and dates. In the final phase a prediction algorithm applying data mining and machine learning techniques was developed, based on the historical data, which is used as a small sample of big data that could be potentially generated by the IoT enabled equipment interconnected via the internet enabling them to send and receive data. Using the K-Nearest Neighbour (KNN) classifier a performance accuracy of 94% was achieved which allows the user to predict future behaviour of the equipment