Automation with the use of Raspberry Pi to Study the Pneumatic Transport of Grains Yuri Silva Cruz Storino, Carlos José de Lima

Abstract

Abstract: Process automation was of great value to industrial development, being an important pillar for its evolution, bringing efficiency in data acquisition and having results in less time. New research in the area of automation, mainly in industrial processes, has been developed for central process controllers. A new technology also used for automation is the Raspberry Pi microcomputer, which can be described as a computer in a smaller format and at a reduced cost, with more important resources such as internet controllers, video, peripherals, and others, in addition to serial communication, OPC (Open Platform Communication) and so on. One area of the industry that currently depends on automation is grain transport, including pneumatic transport, which uses fluids (gaseous) in motion in a pipe (fluiddynamics) to transport grains such as rice, corn, soybeans, and others. Despite being a comprehensive area, there is still much to be studied, requiring the development of automation to increase the efficiency of these studies. The objective of this work is to develop an automation with Raspberry Pi aimed at a study plant of pneumatic transport of grains, using pressure and humidity sensors, and to control a motor connected to the supply of grains to the pipeline. Programming in Python language will be applied for this, creating communication with the sensors, controlling the engine, and interacting with the user through an application via monitor and mouse. For installation in the pneumatic conveyor pipes, an encapsulation was developed for the sensors, which previously had calibration tests done to validate both the sensor and the encapsulation. After this validation, the installation will be done and through the application developed for the Raspberry Pi, a faster and more accurate response of the acquired data is expected, easier control and reading of the plant and the reduction of human errors in conducting the experiments.