Fouling build-up is one of the most challenging problems for heat exchangers in industry. The presence of fouling leads to a degradation of system efficiency, an increase in operating cost, and possibly, a harmful environmental impact. For this reason, fouling analysis has become an extremely important research subject in order to have a safe and efficient operation. The analysis is more difficult where phase change of fluids is involved during the heat transfer process, as in the case of boilers and condensers, which are critical units in industrial facilities. Due to the lack of a comprehensive review of fouling analysis for the case of multiphase heat exchangers, this paper examines available approaches and techniques used for fouling characterization, modeling, monitoring, and prediction in heat exchangers for both single-phase and multiphase heat exchangers with a focus on fouling in thermal desalination systems. It also gives an overview of heat exchanger condition monitoring solutions available in the market.

Research and Development becomes one of the main pillars to build knowledge based economy nowadays. In highly competitive market, there is a real need for efficient mechanisms to have a successful technology development model. Open Innovation through University Industry Collaboration (UIC) is one of promising mechanisms to develop new technologies that can seed national knowledge economy. UIC model brings valuable benefits to both academia and industry in order to have efficient technology development processes. In addition, UIC offers university researchers an opportunity to have an exposure to industry and creates training and internship opportunities for university students. Trilateral collaboration model, which adds end-user to UIC arrangement, could bring an additional advantage to align the product development with actual customer needs making introduction of new product more successful. This paper gives an overview about the main aspects of trilateral collaboration and shows a real trilateral collaboration case between academia, technology provider and end-user to develop an actual product that serves end-user needs showing advantages and challenges of proposed model.

Summary: Maximizing oil recovery of the gas-oil separation plant (GOSP) is intended to increase revenue in the oil and gas industry. The GOSP is an integral part of the petroleum industry, and it consists of multistage separators, a heater-treater, desalination, a stabilization column, and a stock tank of oil. It is conventional practice to operate the GOSP at fixed operating conditions without considering the effects of variation for different parameters such as ambient temperature, chemical composition, reboiler (60°C, 65°C, and 70°C), and stabilization (temperature and pressure). Optimizing the GOSP parameters can help to maximize the GOSP oil recovery and, as a result, increase the revenue and profit. This study aims to optimize operation parameters to maximize the oil recovery of the GOSP at which the maximum oil recovery can be obtained from the GOSP. To achieve the objective of this study, first, a GOSP model was built using Petro-SIM process simulator software for a typical Saudi Aramco GOSP. The input data for the process simulator were the data from the initial pressure/volume/temperature (PVT) analysis. Optimizer tools in Petro-SIM were used to estimate the optimal conditions of the GOSP for achieving maximum oil recovery. The results showed that the optimization of the GOSP parameters such as ambient temperature, high-pressure separator (HPS), low-pressure separator (LPS), reboiler temperature, and stabilization pressure and temperature have a significant effect on the GOSP oil recovery and lead to increased revenues. Adjusting the HPS and LPS pressures to the optimal values at each ambient temperature significantly improves the GOSP oil recovery and can generate extra revenue of more than USD 500 million for 3 months considering the typical climate condition and GOSP (50,000 B/D capacity) in Saudi Arabia.

Variations in process variables are one of the major concerns in industrial plants due to their impact on product quality and equipment performance, leading to higher operating and maintenance costs in addition to safety issues. One of the common sources of process variation is control valve stiction. Therefore, early detection and quantification of valve stiction is crucial for maintaining product quality and reducing maintenance costs. A smart valve equipped with a positioner simplifies stiction diagnosis, whereas the diagnosis process for an ordinary control valve is not straightforward. As the number of ordinary control valves installed in plants is still quite high, we developed in this research a smart approach for stiction detection and quantification for this type of control valve. In order to implement the proposed approach, we used state-of-the-art signal processing and machine learning techniques to develop a trustworthy strategy for stiction analysis applicable to a wide variety of operating setups, such as cascade loops and settings variations. Testing this approach on publicly available industrial data showed that it worked well for all tested scenarios to detect the stiction and provided an accurate estimate of the stiction value.

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms.

In the oil and gas industries, the gas-oil separation plant (GOSP) is operated at fixed operating conditions without considering the effect of ambient temperature (Ta) variations. Temperature is one of the parameters that can affect the GOSP process and its output. Ignoring the variation in the ambient temperature may lead to a loss in oil recovery and corresponding revenue. The separation process is highly affected by the variation of ambient temperature, where the ambient temperature varies greatly from summer to winter. As the plant is operated at a fixed operating condition that is not optimized results in low recovery of the GOSP output. The optimization process of high-pressure separator (HPS) and low-pressure separator (LPS) is required to compensate for the variation in ambient temperature, which leads to maximizing oil recovery and plant revenue. Thus, this study aims to develop an intelligent optimization system to improve the oil production of the Gas-Oil-Separation-Plant (GOSP) considering the variation in ambient temperature. Accordingly, the objective of this work is to provide an intelligent system for the determination of the best-operating set-points of the pressure (optimum pressure) for the high-pressure separator (HPS), low-pressure separator (LPS) at which maximum hydrocarbon liquid recovery can be obtained through the GOSP plant at a given ambient temperature. To achieve the objective of this study, a GOSP model was built by Petro-SIM process simulator software using a typical Saudi Aramco GOSP design. The data from the initial PVT analysis is considered as an input for the process simulator. Then the optimizer tools built in the Petro-SIM are activated to determine the optimum condition of HPS and LPS in an integrated fashion accounting for variation in ambient temperature for maximizing the liquid recovery. The results showed increases in ambient temperature result in a decrease in the oil recovery of the GOSP plant. The oil recovery appears to increase with an increase in HPS pressure reaching the maximum and then decreasing with a further increase in the HPS pressure. Similarly, the LPS pressure affects the oil recovery when LPS pressure increases oil recovery increases reaching the optimum point and then decreases with a further increase in LPS pressure. An intelligent system can be built based on an optimization process for determining the optimum condition of LPS and HPS for compensating variation in ambient temperature.