Futuristic Air Compressor System Design and Operation by Using Artificial Intelligence

Indiana University-Purdue University Indianapolis (IUPUI)The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.2019-12-0

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems

This paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances

MODELLING OF LINEAR PERMANENT MAGNET MOTOR FOR AIR-VAPOR COMPRESSOR

Power consumption of refrigerator is the top three among the various household appliances. This is because of the lack performance and efficiency of the conventional refrigerator compressor system. This paper describes about the design of linear permanent magnet motor for reciprocating air-vapor compressor application. There are various types of linear motor technologies and topologies for air-vapor compressor that have been discussed, such as, linear induction machine, linear synchronous machine, linear DC machine, and linear permanent magnet machine. The significant designs criteria considered are based on their force capability, higher efficiency, simplicity and cost-effectiveness. Among the linear motor technologies reviewed, a linear permanent magnet machine is the most preferable technologies for the reciprocating air-vapor compressor application due to the high thrust capability and efficiency. There are three categories of the linear permanent magnet, which are, moving-coil, moving-iron, and moving-magnet. This paper is mainly focused on the moving-magnet topologies which considered a tubular permanent magnet, a slotted and a slotless stator, and also a different type of magnet configuration for the reciprocating air-vapor compressor application. The linear permanent magnet topologies have been studied and analyzed in order to obtain the best three designs for the reciprocating air-vapor compressor application. ANSYS software, ANSOFT Maxwell, is used to draw and analyze the proposed designs to get the results of air-gap flux distribution, air-gap flux density and the respective graph. The result for the three designs will be compared discussed in order to choose one best design for air-vapor compressor application. In conclusion, the best design obtained can be apply for air-vapor compressor to increase efficiency, performance and reduce the energy consumption as well

Improving the Accuracy of Industrial Robots via Iterative Reference Trajectory Modification

In this paper, a novel repetitive control (RC) scheme is presented and discussed. The general framework is the control of repetitive tasks of robotic systems or, more in general, of automatic machines. The key idea of the proposed scheme consists in modifying the reference trajectory provided to the plant in order to compensate for external loads or unmodeled dynamics that cyclically affect it. By exploiting the fact that uniform B-spline trajectories can be generated by means of dynamic filters, the trajectory planning phase has been integrated within an RC scheme that is able to modify in real time the reference signal in order to nullify the tracking errors occurring at the desired via-points. Because of this mechanism, the control scheme is very suitable for the application to industrial plants with off-the-shelf, unmodifiable controllers. Experimental results obtained with a standard industrial manipulator both in joint space and in workspace show the effectiveness of the proposed method

A novel linear motor for a linear refrigeration compressor: modelling, measurement and sensor-less stroke detection

With the increasing global awareness of the environmental conservation, linear compressors have attracted growing attention with their applications in domestic and cryogenic refrigeration systems. A linear compressor is driven directly by a linear motor and the free-piston design allows piston stroke to be variable. An active control of stroke prevents piston-cylinder collision and enables efficient cooling capacity modulation. This thesis introduces the performance of a novel moving magnet type linear compressor/motor and investigates the approaches to sensor-less stroke detection. An experimental test facility incorporating the linear compressors into a vapour compression refrigeration system was introduced, in which piston displacement was measured with a displacement sensor. The piston stroke and offset were controlled with PID controllers implemented in LabVIEW. To investigate the characteristics of the moving magnet linear motor, a finite element analysis (FEA) model was built in ANSYS Maxwell 19.2. Simulations were validated through static force measurements. Force constant was given by the static shaft force against current. Saturation can be observed with the increase of current. A smaller saturation current was shown for a larger armature displacement. For the purpose of increasing cooling capacity of the linear compressor, operations with small axial clearance volumes were considered. Refrigeration performance using R1234yf as refrigerant with various clearance volumes and with an offset of 0 mm were experimentally compared. The cooling capacity for a pressure ratio of 2.5 and a stroke of 13 mm increases by 12% as the clearance decreases from 1.07 mm to 0.4 mm. Piston stroke detection without a displacement sensor reduces the cost and facilitates the stroke control especially in miniature linear compressors. An artificial neural network (ANN) based stroke detection was presented. Fast Fourier transform (FFT) analysis was performed on current and voltage signals to extract harmonic terms as inputs of the neural network model to predict the stroke. The ANN technique can achieve a good accuracy for most of the cases, but reliability remains a problem. A more reliable sensor-less stroke detection technique based on flux linkage variation using inductive coils was proposed. The technique requires resonant operation. A 1D (One-Dimensional) electromagnetic model and a 3D (Three-Dimensional) FEA model were built to compute the flux linkage variations. The open-circuit flux linkage in each core produced by NdFebB magnets varies linearly with the piston displacement. Flux linkage difference at two zero-crossing points of current was used to infer stroke. The proposed low-cost sensor-less stroke detection technique can achieve error of only 4%. The adoption of this novel technique is crucial to the commercialization of free-piston machines for high efficiency