Compressor Computerized Performance Monitoring System CPMS

Background information Natural gas liquid (NGL) production facilities, typically, utilize turbo-expander-brake compressor (TE) to generate cold for C2+ separation from the natural gas by isentropic expansion of feed stream and use energy absorbed by expansion to compress residue gas. Experience shows that during operational phase TE can exposed to operation outside of design window that may lead to machine integrity loss and consequent impact on production. At the same time, there is a lack of performance indicators that help operator to monitor operating window of the machine and proactively identify performance deterioration. For instance, TE brake compressor side is always equipped with anti-surge protection system, including surge deviation alarms and trip. However, there is often gap in monitoring deviation from stonewall region. At the same time, in some of the designs (2×50% machines) likelihood of running brake compressor in stonewall is high during one machine trip or train start-up, turndown operating modes. Also, typical compressor performance monitoring systems does not have enough dynamic parameters that may indicate machine process process performance deterioration proactively (real-time calculation of actual polytrophic efficiency, absorbed power etc.) and help operator to take action before catastrophic failure occurs. In addition, typical compressor monitoring systems are based on assumed composition and fixed compressibility factor and do not reflect actual compositions variations that may affect machine performance monitoring. To overcome issues highlighted above, Hawiyah NGL (HNGL) team has developed computerized monitoring and advisory system to monitor the performance of turbo-expander-brake compressor, proactively, identify potentially unsafe conditions or performance deterioration and advice operators on taking necessary actions to avoid unscheduled deferment of production. Computerized performance monitoring system has been implemented in HNGL DCS (Yokogawa) and utilized by control room operators on day-to-day basis. Real-time calculation, analysis and outputs produced by performance monitoring system allow operator to understand how current operating condition are far from danger zone. Proactive deviation alarms and guide messages produce by the system in case of deviation help operators to control machine from entering unsafe region. Actual polytrophic efficiency, adsorbed power calculations provide machine condition status and allow identifying long-term performance deterioration trends.

Detecting a Novel Walking-Based Performance Fatigability Marker With Accelerometry in Older Adults

Walking-based performance fatigability measures (e.g., lap-time difference) may not adequately capture performance deterioration as self-pacing is a common compensatory strategy in those with low activity tolerance. To overcome this limitation, we developed a new approach with accelerometry (ActiGraph GT3X+, sampling=80 Hz, non-dominant wrist) during fast-paced 400m-walk (N=57, age=78.7±5.7 years, women=53%). Cadence (steps/second) was estimated using raw accelerometer data (R “ADEPT”). Penalized regression splines (R “mgcv”) were used to estimate the individual-level smoothed cadence trajectories. “Time-to-slow-down” was defined as first time-point where the full confidence interval of change in cadence<0. Five participants were censored at stopping time (not slowdown or complete walk). Median “time-to-slow-down” was 1.86 minutes (IQR=0.98-2.73, range=0.57-6.25). Participants with longer “time-to-slow-down” had slower starting cadence, longer 400m-walk time, and greater perceived fatigability (Pittsburgh Fatigability Scale), p’s<0.05 (linear regression). Our preliminary findings revealed that detecting accelerometry-based performance fatigability/deterioration in older adults is feasible and needs to account for initial pace.

An empirical investigation on the correlation between solar cell cracks and hotspots

In recent years, solar cell cracks have been a topic of interest to industry because of their impact on performance deterioration. Therefore, in this work, we investigate the correlation of four crack modes and their effects on the temperature of the solar cell, well known as hotspot. We divided the crack modes to crack free (mode 1), micro-crack (mode 2), shaded area (mode 3), and breakdown (mode 4). Using a dataset of 12 different solar cell samples, we have found that there are no hotspots detected for a solar cell affected by modes 1 or 2. However, we discovered that the solar cell is likely to have hotspots if affected by crack mode 3 or 4, with an expected increase in the temperature from 25$$^\circ $$ ∘ C to 100$$^\circ $$ ∘ C. Additionally, we have noticed that an increase in the shading ratio in solar cells can cause severe hotspots. For this reason, we observed that the worst-case scenario for a hotspot to develop is at shading ratios of 40% to 60%, with an identified increase in the cell temperature from 25$$^\circ $$ ∘ C to 105$$^\circ $$ ∘ C.

The Severity of Muscle Performance Deterioration in Sarcopenia Correlates With Circulating Muscle Tissue-Specific miRNAs

Sarcopenia is defined as an age-associated loss of skeletal muscle function and muscle mass and is common in older adults. Sarcopenia as a disease is currently of interest not only to orthopedists and surgeons but also to internists, endocrinologists, rheumatologists, cardiologists, diabetologists, gynaecologists, geriatricians and paediatricians. In cooperation with the 5th Internal Medicine Clinic, we, as a unit of clinical research, aimed to describe a sarcopenic specific miRNA expression profile for disease diagnostics and classification of the severity of muscle performance deterioration. This study included a total of 80 patients (age 55-86 years) hospitalized at the V. Internal medicine clinic of LFUK and UNB with different severity of muscle performance deterioration. The study participants were evaluated and classified according to short physical performance battery score (SPPB). In this study, we investigated the role of circulating miRNAs in sarcopenia in the elderly. We hypothesized that sarcopenia effects the expression of muscle tissue-specific miRNAs (MyomiRNAs), which could be potentially reflected in the blood plasma miRNA expression profile. The expression of specific circulating miRNAs in patients with different muscle performances was analyzed. Patients’ blood plasma was evaluated for the expression of myomiRNAs: miRNA-29a, miRNA-29b, miRNA-1, miRNA-133a, miRNA-133b, miRNA-206, miRNA-208b and miRNA-499, and the data were correlated with diagnostic indicators of the disease. We showed a specific sarcopenia miRNA profile that could be considered a possible biomarker for the disease. Patients with low muscle performance showed increased miRNA-1, miRNA-29a and miRNA-29b expression and decreased for the miRNA-206, miRNA-133a, miRNA-133b, miRNA-208b and miRNA-499 expression. We show that the severity of muscle performance deterioration in sarcopenia correlates with specific miRNA expression. We also propose the profile of miRNAs expression in blood plasma as a specific biomarker for sarcopenia diagnostics. Future clinical studies will be necessary to eventually naturally have to elucidate the underlined molecular mechanism responsible for specific miRNAs expression in sarcopenia pathology and progression of the disease.

Sentiment Weighted Word Embedding for Big Text Data

Sentiment analysis is the practice of eliciting a sentiment orientation of people's opinions (i.e. positive, negative and neutral) toward the specific entity. Word embedding technique like Word2vec is an effective approach to encode text data into real-valued semantic feature vectors. However, it fails to preserve sentiment information that results in performance deterioration for sentiment analysis. Additionally, big sized textual data consisting of large vocabulary and its associated feature vectors demands huge memory and computing power. To overcome these challenges, this research proposed a MapReduce based Sentiment weighted Word2Vec (MSW2V), which learns the sentiment and semantic feature vectors using sentiment dictionary and big textual data in a distributed MapReduce environment, where memory and computing power of multiple computing nodes are integrated to accomplish the huge resource demand. Experimental results demonstrate the outperforming performance of the MSW2V compared to the existing distributed and non-distributed approaches.

Multifunctional Partially Reflective Surface for Smart Blocks

In general, a partially reflective surface (PRS) is mainly used to increase the gain of an antenna; some metallic objects placed on the PRS degrades the antenna performance because the objects change the periodic structure of the PRS. Herein, we propose a multifunctional PRS for smart block application. When a passenger passes over a smart block, the fare can be simultaneously collected and presented through the LED display. This requires high gain antenna with LED structure. The high gain characteristic helps the antenna identify passengers only when they pass over the block. The multifunctional PRS has a structure in which an LED can be placed in the horizontal direction while increasing the antenna gain. We used the antenna’s polarization characteristics to prevent performance deterioration when LED lines are placed in the PRS. We built the proposed antenna and measured its performance: At 2.41 GHz, the efficiency was 81.4%, and the antenna gain was 18.3 dBi. Furthermore, the half-power beamwidth was 18°, confirming a directional radiation pattern.

Considerations for the Extension of Gas Path Analysis to Electrified Aircraft Propulsion Systems

Aircraft operators rely on gas path analysis techniques for monitoring the performance and health of their gas turbine engine assets. This is accomplished by analyzing discernable shifts in measurement parameters acquired from the engine. This paper reviews the founding mathematical principles of gas path analysis, including conventional approaches applied for estimating engine performance deterioration. Considerations for extending the application of gas path analysis techniques to Electrified Aircraft Propulsion (EAP) systems is also discussed, and simulated results from their application to an EAP concept comprised of turbomachinery and electrical system hardware is provided. Results are provided comparing the parameter estimation accuracy offered by taking a whole-system approach towards the problem setup versus that offered by analyzing each subsystem individually. For the latter, the importance of having accurate direct or inferred measurements of external mechanical torque loads placed upon turbomachinery shafts is emphasized.