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Among the construction processes of Portland cement concrete pavement (PCCP), the curing compound spraying process is one of the most important processes. If the curing compound spraying amount does not meet the standard or if the curing compound is not applied evenly, distresses occur at the early age of construction, ultimately causing deterioration in concrete pavement performance. The purpose of this study is to develop a real-time monitoring system for a curing compound spraying process based on the Internet of Things (IoT) and sensing technologies to improve the construction quality of concrete pavement. To achieve the goal of this research, we conducted various laboratory and field experiments. The curing compound spraying amount and sprayed status were measured and analyzed using flowmeters, image acquisition sensors, and an image processing program, and the data were provided to workers in real time and simultaneously transmitted to the IoT cloud to form a database. From this study, it is confirmed that the IoT-technology-based curing compound spraying amount and sprayed status monitoring systems can be successfully established to manage construction quality related to the curing of concrete pavement.

Multiphase flowmeters (MPFMs) measure the flow rates of oil, gas, and brine in a pipeline. MPFMs provide remote access to real-time well production data that are essential for efficient oil field operations. Most MPFMs are complex systems requiring frequent maintenance. An MPFM that is operationally simple and accurate is highly sought after in the energy industry. This paper describes an MPFM that uses only pressure sensors to measure gas and liquid flow rates. The design is an integration of a previously developed densitometer with an innovative Venturi-type flowmeter. New computing models with strong analytical foundations were developed, aided by empirical correlations and machine-learning-based flow-regime identification. A prototype was experimentally validated in a multiphase flow loop over a wide range of field-like conditions. The accuracy of the MPFM was compared to that of other multiphase metering techniques from similar studies. The results point to a robust, practical MPFM.

The real-time monitoring of fluid properties in tubular systems, such as viscosity and flow rate, is essential for industries utilizing liquid mediums. Nowadays, most studies of the fluid characteristics are performed off-line using laboratory facilities that can provide accurate results, yet they do not match the demanded industrial pace. Off-line measurements are ineffective and time-consuming. The available real-time monitoring sensors for fluid properties are generally destructive methods that produce significant and persistent damage to the tubular systems during the installation process. Others use huge and bulky invasive instrument methods that generate considerable pressure reduction and energy loss in tubular systems. For these drawbacks, industries centered their attention on non-invasive and non-destructive testing (NDT) methodologies, which are installed on the outer tubular surface to avoid flow disturbance and desist shutting down systems for installations. Although these sensors showed excellent achievement for monitoring and inspecting pipe health conditions, the performance was not convincing for monitoring the properties of fluids. This review paper presents an overview of the real-time monitoring of fluid properties in tubular systems for industrial applications, particularly for pipe monitoring sensors, viscosity, and flow measurements. Additionally, the different available sensing mechanisms and their advantages, drawbacks, and potentials are discussed.

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor's streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

A thermo-anemometric flowmeter design and the principles of its work are presented in the article. A mathematical model of the temperature field in a stream of biofuel is proposed. This model allows one to determine the fuel consumption with high accuracy. Numerical modeling of the heater heat balance in the fuel flow of a thermo-anemometric flowmeter is conducted and the results are analyzed. Methods for increasing the measurement speed and accuracy of a thermo-anemometric flowmeter are proposed.

After studying the performance and characteristics of actual laminar flowmeters a new disposition for this type of sensors is proposed in such a way that the measurement errors introduced by the intrinsic nature of the device can be minimized. The preliminary study shows that the developing entry region introduces non-linearity effects in all these devices. These effects bring about not only errors, but also a change in the slope of the linear calibration respect of the Poiseuille relation. After a subsequent analysis on how these non-linearity errors can be reduced, a new disposition of this type of flowmeters is introduced. This device makes used of flow elements having pressure taps at three locations along its length and connected to three isolated chambers. In this way, the static pressure can be measured at three locations and contributed to by the pressure taps at the level of each chamber. Thus the linearization error is reduced with an additional advantage of producing a reduced pressure drop.