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With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters' performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings' Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of −10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices. [ABSTRACT FROM AUTHOR]

Wireless technology is increasingly significant in today's industrial landscape. Standards like WirelessHART, ZigBee, and ISA100.11a are being widely used. However, despite their widespread use, wireless networks may sometimes be susceptible to packet loss or drops, making closed-loop systems vulnerable and resulting in system failure. To prevent such issues, dead-time compensation is necessary. The conventional techniques of predictive PI are commonly used for this purpose. Still, they must perform optimally for wireless networks with dead time, and set-point variations can affect network stability. To address this, a fractional calculus-based predictive PI compensator is proposed in this paper for wireless networks in process industries to improve the performance of these compensators. Industrial processes that involve wireless measurement and control actions in the pressure process model are used to evaluate the proposed compensator. The wireless network's performance is assessed for packet loss, reduced throughput, and increased latency, and the proposed compensator outperforms traditional ones to achieve better set-point characteristics. [ABSTRACT FROM AUTHOR]

The gas sweetening process removes hydrogen sulfide (H2S) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of H2S in sweet gas is crucial to avoid operational and environmental issues. This study shows the capability of artificial neural networks (ANN) to predict the concentration of H2S in sweet gas. The concentration of N-methyldiethanolamine (MDEA) and Piperazine (PZ), temperature and pressure as inputs, and the concentration of H2S in sweet gas as outputs have been used to create the ANN network. Two distinct backpropagation techniques with various transfer functions and numbers of neurons were used to train the ANN models. Multiple linear regression (MLR) was used to compare the outcomes of the ANN models. The models' performance was assessed using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate that ANN trained by the Levenberg–Marquardt technique, equipped with a logistic sigmoid (logsig) transfer function with three neurons achieved the highest R2 (0.966) and the lowest MAE (0.066) and RMSE (0.122) values. The findings suggested that ANN can be a reliable and accurate prediction method in predicting the concentration of H2S in sweet gas. [ABSTRACT FROM AUTHOR]

Achieving reliable power generation from Dry Low Emission gas turbines together with low CO2 and NOx discharge is a great challenge, as the rigorous control strategy is susceptible to frequent trips. Therefore, it is crucial to establish a dynamic model of the turbine (such as the one commonly attributed to Rowen) to ascertain the stability of the system. However, the major distinctive fuel system design in the DLE gas turbine is not constructed in the well-established model. With this issue in mind, this paper proposes a modelling approach to the DLE gas turbine fuel system which consists of integrating the main and pilot gas fuel valve into Rowen's model, using the First Principle Data-Driven (FPDD) method. First, the structure of the fuel system is determined and generated in system identification. Subsequently, the validated valve models are integrated into Rowen's model as the actual setup of the DLE gas turbine system. Ultimately, the core of this modelling approach is fuel system integration based on the FPDD method to accurately represent the actual signals of the pilot and main gas fuel valves, gas fuel ow and average turbine temperature. Then, the actual signals are used to validate the whole structure of the model using MAE and RMSE analysis. The results demonstrate the high accuracy of the DLE gas turbine model representation for future utilization in fault identification and prediction study. [ABSTRACT FROM AUTHOR]