Neural net implementation of steam properties on FPGA

Abstract

Real time applications like model predictive control, monitoring and data reconciliation of power plants and industrial processes employ nonlinear mathematical models and require thermodynamic properties and their derivatives of working fluids. Applications like super heater temperature control based on energy balance and real time data reconciliation, require an efficient and a compact method for simultaneous estimation of thermodynamic properties, and their partial derivatives suitable for implementation in field-programmable gate array (FPGA). However, the complex mathematical formulations of these properties prohibit direct implementations in FPGAs. Single artificial neural network (ANN) architecture is used to replace the entire code in higher level languages, running into a few thousand lines. FPGA implementation of a compact neural network for the entire range of thermodynamic properties is presented. Large arguments in sigmoid function are factored into a product of integer and a fractional part which is represented using series approximation with five terms only and the integers are represented in look up table (LUT). This ensures optimum storage and computational burden for the above applications. The ANN is implemented in IEEE 754 floating point with synthesis in Xilinx ISE design suite using Verilog HDL. The results are presented for a typical pressure versus saturation temperature.

Reconfigurable linear feedback shift register for wireless communication and coding

“…The simulation waveform is taken from the Xilinx ISIM simulator [23], [24]. The simulation provides the hardware and timing reports for further analysis [25]. The test inputs and outputs help to understand the functionality of design.…”

Implementation of first order statistical processor on FPGA for feature extraction

“…Moreover, FPGAs prove their suitability for implementing compact neural networks that replace extensive code in higher-level languages for estimating thermodynamic properties and their derivatives in real-time applications. This allows for efficient computation and storage, crucial for applications like model predictive control and monitoring of power plants and industrial processes [25]. Additionally, FPGAs excel in real-time acquisition and processing of biomedical signals, as demonstrated in the proposed platform for acquiring and processing electroencephalographic (EEG) signals.…”