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Energy Saving in Industrial Processes Using Modern Data Acquisition
One of the most effective industrial process improvement technologies is model predictive control, neural networks, and soft sensor technologies. Technologically advanced manufacturing companies that use innovative process management and monitoring systems achieve 20-30% lower production costs than similar plants that do not use such systems. The idea of this work is to evaluate the potential of a cognitive industrial process management system to optimize the company’s activities in energy efficiency and resource conservation. For this, advanced methods of data analysis and collection, monitoring, control system are used.
Process optimization has the following steps:
Data acquisition Can be realized using various techniques. Using hardware sensors is the most commonly used method for data acquisition. This is the easiest way to monitor the process if there is no monitoring system. If a data acquisition system (e.g. SCADA) is already installed, it is possible to use SCADA acquired data or, if SCADA does not collect all necessary data, it is possible to combine SCADA acquired data with data collected with additional sensors.
Received data usually needs to be transferred For further processing. There are two major types of data transfer: wired, wireless, and software. Generally data transfer speed is not important for acquisition systems, as large amounts of data are not involved. Wired and wireless data transfer are used to transfer data from hardware sensors. Both raw and processed data can be transferred for collection. It is possible to use different types of data transfer methods in one system. Software links are used to collect data from other, already installed systems, using standard data transfer protocols such as SCADA, OPC and DATA sockets.
For data processing Most standard data processing software is used. The most popular software is Matlab, other software solutions are Profissignal, Labview and custom software designed for specific tasks.
There are 2 main algorithm types for controlling complex systems.
1. Model predictive control.
2. Advanced process control.
Model predictive control (MPC) collects information about the system process, learns the specific sequence of changing parameters, predicts them, and changes the system parameters to keep the system output smooth. MPC is used in systems that monitor and control certain variables.
Advanced process control (APC) controls complex processes with many monitored variables and controlled outputs. Compared to MPC, APC involves more data processing.
In this paper we improve the efficiency of an industrial process in a pulp paper manufacturer by improving the compressed air system in the product packing line. The system consists of 3 air compressors and 217 users (actuators, valves, etc.). The pressure in the system is set between 6,5 and 7,9 bar. Average pressure in the system – 7,2 bar. The pressure deviation in the system is ± 0,7 bar. Users with the highest pressure demands require an air pressure of 6 bar.
Improving performance involves the following steps:
1. To analyze the existing system.
2. Monitor the compressed air system using a data logger.
3. Analyzing data from data loggers.
4. Upgrading compressed air system with additional controllers, sensors (if required) and preparing APC.
5. Formulation of Algorithm for APC.
Modern data loggers and current, pressure, dew point and consumption sensors are used for temporary industrial process monitoring. The collected data is used to find the weak points in the compressed air system to create a necessary system upgrade list and to create an efficient control algorithm for the controller installed in the next phase. Test results showed that all system outputs averaged at 39.2% of its maximum productivity.
System pressures on 6 bars are unnecessarily high. This increases the amount of air blown out of the system, wear of pneumatic system components, and energy consumption.
The following improvements have been made to improve the compressed air system:
1. Install the system on the APC controller.
2. Installation of pressure and flow sensors.
3. Installation of air compressor controllers.
Installing an APC controller in a compressed air system will provide greater flexibility to control all system parameters and increase efficiency.
The original system did not have direct control over the air compressors, they could only be turned on or off at 100% output. We added in a system controller that could operate the air compressor between 20%-100% of total output. This update will increase the control flexibility and efficiency of the compressed air system.
For the first upgraded system test, the run controller and connected sensors were enabled, the compressed air pump controller was disabled, the system pressure was set to 7,2 bar with a possible variation of ± 0,1 bar, so the deviation is 7 times lower than the original system, the same as the base setup. System pressure target left. The system performed well and saved 2,1% electricity compared to the original system.
On the second test run the air compressor controllers were enabled, the system pressure was set to 6,25 bar with a deviation of ±0,1 bar. The test results are very stable and have a power saving of 15,1 % compared to the original system.
The completed system is very compact. It is estimated that the lifetime of various system components will increase by 5-10%. A system using an APC controller will save 15% in energy and 5-10% in hardware wear. Also APC systems can be maintained remotely which saves money on servicing costs. Total system savings is at least 25%. A designed and installed APC system will procure in less than 6 months.
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#Energy #Saving #Industrial #Processes #Modern #Data #Acquisition