Cognitive sensor for amount of filler [CAS2] - INTEGRATED
Creators: name: Vega, Cristina
name: Reñones, Anibal
name: Sanz, Laura
Publisher: name: Zenodo
Year: 2022
Subjects: Asphalt
cognition
Automation
Process Industry
innovation
H2020
System Identification Model
R&amp;D
IoT
cognitive sensors

description: This cognitive sensor, CAS2, is developed to estimate and measure the fine filler quantity that goes out of the aggregates drying process and exits both to the baghouse filter and to the hot bins. The high-level outcome of this cognitive sensor is to obtain the real amount of filler flow present in the mix of aggregates, which will allow to waste less energy in the rotary drying drum and in the filtering (baghouse) process. CAS2 output results have been integrated in the corresponding platform developed for the Asphalt domain CAS2 sensor aims to provide an estimation of flow of filler during the drying process of the aggregates. As such, the raw measurements need to be adjusted to compensate the undesired noise when the aspiration takes place as there is no aggregates in the drum to be dried. To compensate for this noise, a model based on the actual aspiration pressure has been created. The model obtained is explained below, together with the OTHER assets shared. <strong>CAS2_dataset_5.RData: dataset of raw data and aspiration pressure.</strong> The dataset shown in Figure 8 has been used for the creation of the model. The model tries to estimate the vibration measured (ACEL1_20-25 measured in g<sub>RMS</sub>) based on the aspiration pressure (variable named as RPA2100 measured in mmH<sub>2</sub>O). <strong>CAS2_DMP_dataset5: Data Management Plant of the CAS2_dataset_5.RDataCAS2. </strong> This file includes the Data Management Plan associated to this data used. <strong>CAS2_sourcecode_1.R: program used to estimate, graph and evaluate the model.</strong> This file is an algorithm programmed in an open source R programming environment and language. This algorithm creates a model that relates the suction (x variable) and vibration variables (f function) in the suction process with the dataset described above. The developed model creates a piecewise linear relationship between the two variables for aspiration values as can be seen in Figure 10. It must be also noted that for a certain pressure below a threshold (27 mm H<sub>2</sub>O) the output of the model is 0 as the baghouse does not operate. <strong>CAS2_sourcecode_2.sql: Program to create the table of values for CAS2 solution.</strong> The source code shows two different parts. The first one is the creation of the table with the whole set of parameters needed from the CAS2 information model into the database of the CAP. The next is explained in section 6.1.2. Date time of the measurement. Raw value of the sensor (in g<sub>RMS</sub>). Measured pressure (in mm<sub>H2O</sub>) at the IoT system. Calculated value of the sensor based on the model equation shown above and done at the local IoT embedded system. Calculated value of the sensor based on the model equation shown above and done at the platform. Measured pressure (in mm<sub>H2O</sub>) as registered by the CAP from the Production dataframes explained in this section. <strong>CAS2_video_5.mp4: </strong><strong>Model implementation at IoT local system in asphalt pilot plant.</strong> This video shows the output of the local IoT embedded system located in the Asphalt pilot plant and how the model estimated previously (see section 0 for model explanation) is running locally at the asphalt plant. The resulting model output is proportional to the actual filler flow during drying and can be compared to the same processing done online in the database, as explained below, to effectively validate the calculations done by the IoT system. <strong>CAS2_sourcecode_2.sql: Program to process CAS2 IoT measurements in the database.</strong> Source code that shows the step to recreate the calculations based on the model previously shown but calculated directly in the population of the CAS2 table in the database with the use of a trigger, which is procedural code that is automatically executed in response to certain events on a particular table or view in a database. In the case of CAS2, each time a new MQTT output is sent from the IoT CAS2 system to the CAP together with the pressure measured from the production frames as explained in section 2, the trigger function ‘<em>processStreamCAS2</em>’ is fired and it applies the model estimated (see section 6.1.2 for model explanation) and populates the cognitive solution table CAS2 appropriately. <strong>CAS2_video_6.mp4: </strong> <strong>MQTT dataframes received from local IoT at CAP platform.</strong> In this video it is shown how, in real time, the MQTT agent (mosquito) receives the values from the CAS2 local IoT system into the CAP. <strong>CAS2_video_7.mp4: Population of CAS2 database in the CAP platform.</strong> In this video, it is shown how the new table <em>CAS2</em> of the database is populated with the IoT frames together with the calculated values based on the SQL trigger source code shown earlier.