Extensive testing is crucial to ensure exhaust emission compliance for high-horsepower (H-hp) commercial engines. Back pressure sensors, integral to the exhaust system, are prone to producing noisy measurements due to the turbulent nature of the exhaust gas flow and other testing inaccuracies, mandating the use of a noise reduction filter. The time-consuming process of filter tuning, required to remove excessive process/measurement noise, often involves trialand-error. This practice, entailing numerous experiments with live engines, results in high financial costs and emissions due to challenges in extracting the ground truth signal from noisy measurements. Developing a digital twin (DT) of this system is proposed to expedite filter tuning, hence reducing cost and emissions output. Creating such a DT necessitates a method of back pressure sensor noise classification to accurately simulate the signal. This letter introduces a step-by-step procedure for characterising the H-hp engine back pressure sensor through statistical measures, leading to the development of the DT. This letter demonstrates the potential of this approach in an industrial case study, showcasing its viability for application in engine test-bed facilities and across industries. The economic calculation estimates a potential £184,400 reduction in diesel fuel costs and 321,600 kg of CO2 emissions by tuning the filter through the DT compared to current industrial practice

Batteries are intricate electrochemical devices that demonstrate stochastic and nonlinear behavior contingent upon environmental and operational conditions; therefore, battery monitoring is an essential component throughout their application. This paper introduces a web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an element of Internet of Things (IoT), utilizing the ESP32 microcontroller. The system provides comprehensive online data in real-time through the integration of a multitude of sensors. The proposed system aims to overcome the constraints of current communication technology by capitalizing on the advantages of LoRa, a technology known for its efficient long-range and low-energy transmission, which renders it highly suitable for applications involving real-time monitoring. Furthermore, a control operation allows users to manage critical battery functions, including charging and discharging. The study performed a thorough experimental assessment of the proposed system across various operations, and the outcomes effectively corresponded with the primary objective and goals of the research. The system under consideration successfully facilitates remote monitoring and user control in real-time, provides long-term data visualization through data logging, and enables battery condition evaluation. The implementation of data logging was intended to optimize the utilization of future battery evaluation metrics, including State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). Consequently, the developed system is well-suited for numerous applications that demand efficient energy storage solutions, including Electric Vehicles (EVs) and renewable energy.

This work introduces a method for closed-loop system identification using frequency analysis, employing Empirical Transfer Function Estimation (ETFE). By integrating optimization within a Monte Carlo framework, it enhances the precision of ETFE, resulting in minimized frequency response errors compared to actual system data. Leveraging controller information in an offline model fitting scheme, it achieves optimal realization of process dynamics. The method is evaluated on a data center rack-level cooling system, showing Bode magnitude plots of actual and estimated closed-loop and open-loop dynamics, with confidence intervals demonstrating algorithm consistency. Numerical evaluations confirm the feasibility and potential of the approach to improve offline closed-loop system identification performance in the frequency domain, beneficial for analysis and design. There will not be a comparative study for the introduced approach.

Sugar content is a superior quality indicator for the post-harvesting and ripening status of climacteric fruits. Total soluble solids (TSS) represented by ∘Brix index is a measure of the sugar content. The conventional way to measure TSS is by invasive (IN) technique using manual or digital refractometers. While non-invasive (NIN) infrared refractometer devices have been developed in recent years for various agricultural and food science applications, its accuracy to measure fruit TSS directly is not straightforward, as the device is only dedicated to uses for a specific fruit. This study aims to investigate the suitability of the use of a non-invasive refractometer to measure TSS directly for some post harvested fruits. For comparison, a conventional invasive digital refractometer was also used. Red apples and green apples were used as a reference, while tests were done on bananas, mangos, red pears, golden pears and sapodillas. Among the fruit samples, red pear is the only fruit whose TSS can be measured using the non-invasive refractometer accurately, whereas the device is not suitable for the other fruits due to the significant percentage error and inapplicable corrective calibration value which may lead to a limited prediction of TSS content values.

This article presents the design and monitoring of a lab-scale smart farming system through the integration of control and app designs that can be used for teaching embedded control application to electromechanical systems. A combination of sensors and actuators is used to develop an Arduino-based embedded feedback control system that could be implemented in a smart farming environment. Specifically, we look at controlling electromechanical devices to actuate the fan and water pump to provide the optimal temperature and moisture, respectively, to enhance plant growth in a smart farming setting. The effectiveness of the feedback control is tested by conducting a plant growth experiment. Using garden cress (Lepidium sativum) as a case study, the plant grown in the controlled temperature and moisture settings shows substantially healthier growth compared to the one grown in the non-controlled environment. In addition, an app is designed and developed to transform the Arduino data stream from the sensors into valuable insights that could help the users to monitor and improve the overall crop health. The developed system in this paper enables students to learn integral skills from interdisciplinary engineering fields (e.g. systems, control, mechanical and computer) to solve an agricultural problem.