How To Determine What Link Is Fixed In 4-Bar Analysis Energy Saving in Industrial Processes Using Modern Data Acquisition

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Energy Saving in Industrial Processes Using Modern Data Acquisition

One of the most effective industrial processes improving technologies are model predictive control, neuron networks, and soft sensors technologies. Technologically advanced manufacturing companies, which use innovative processes management and monitoring systems, achieve 20-30% lower production costs than those of similar plants in which such systems are not used. The idea of this work is to evaluate the potential of cognitive industrial processes management systems in order to optimize the company’s activities in increasing energy efficiency and resource conservation. For this purpose are used advanced methods of data analysis and collection, monitoring, control systems.

Process optimization contains following steps:

Data aquision can be realized using different techniques. The most used method for data aquision is to use hardware sensors. This is the simplest way to monitor process if there is no any monitoring system. If data aquision system (example SCADA) is already installed, it is possible to use SCADA acquired data or, if SCADA does not collect all required data, it is possible to combine SCADA acquired data with data collected with additional sensors.

Aquised data usually needs to be transferred for further processing. There are two major kinds of data transfer types: wired, wireless and software. Usually data transfer speed is not important for aquision systems, because there is no large amount of data involved. Wire and wireless data transfer is 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, such as SCADA, using standard data transfer protocols, such as OPC and DATA socket.

For data processing mostly standard data processing software is used. The most popular software is MatLab, other software solutions are Profisignal, LabView and custom made software created for specific task.

There are 2 main algorithm types to control complex systems.

1. Model predictive control.

2. Advanced process control.

Model predictive control (MPC) systems gathers information about process, learns typical sequence of changing parameters, predicts them, and changes system parameters in order to keep system output smooth. MPC are used in systems that monitor and controls few variables.

Advanced process control (APC) controls complex processes with many monitored variables and controlled outputs. Compared to MPC, APC involves much more data processing.

In this paper we improve efficiency of industrial processes in pulp paper manufacturer by improving compressed air system in product packing line. This system contains 3 air compressors and 217 users (actuators, valves etc.). Pressure in system is set to be between 6,5 and 7,9 bar. Average pressure in the system – 7,2 bar. Pressure deviation in system is ± 0,7 bar. The most pressure demanding users require air pressure of 6 bar.

Efficiency improving contains the following steps:

1. Analyzing existing system.

2. Monitor compressed air system using data logger.

3. Analyzing data from data logger.

4. Upgrading compressed air system with additional controllers, sensors (if necessary), and creating APC.

5. Creating algorithm for APC.

Modern data logger and with current, pressure, dew point and consumption sensors is used for temporary industrial processes monitoring. Collected data is used to find weak points in compressed air system make a necessary system upgrades list and create efficient control algorithm for controller that will be installed in next stage. Test results showed that all system output was averaging at 39,2% of its maximum productivity.

Pressures in system above 6 bars are unnecessarily high. It increases amount of leaked air from system, pneumatic system components wear and energy consumption.

To improve compressed air system the following improvements were made:

1. Install to system APC controller.

2. Pressure and flow sensors installation.

3. Air compressors controllers installation.

Installing APC controller to compressed air system will give more flexibility of controlling all system parameters and increase efficiently.

In the original system air compressors had no direct control, they could be only turned on at 100% output or off. We added to system controllers that can make air compressor work between 20%-100% of total output. This update will increase control flexibility and efficiency of compressed air system.

For first upgraded system test run controller and added sensors were enabled, compressed air pump controllers were disabled, system pressure was set to be at 7,2 bar with possible variations of ± 0,1 bar, so deviation is 7 times lower than in original system, while target of system pressure left the same as in base setup. System performed well and compared to original system, 2,1% electricity savings were achieved.

On the second test run air compressor controllers were enabled, system pressure set to be 6,25 bar with deviation of ±0,1 bar. Test results are very stable and with power savings of 15,1 % compared with original system.

Completed system is very compact. It is estimated that various system components lifetime will increase by 5-10 %. System using APC controller will save 15 % energy and 5-10 % in hardware wear. Allso APC systems can be maintained remotely so they save money on servicing costs. Total system savings are at least 25 %. Designed and installed APC system will buy of in less than 6 months.

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