Monitoring Of Key Parameters Research Articles

A parallel and multi-scale probabilistic temporal convolutional neural networks for forecasting the key monitoring parameters of gas turbine

As a crucial equipment in the power industry, gas turbines need effective condition monitoring techniques to maintain safe and reliable operations. Fast yet accurate long-term forecasting of the key monitoring parameters of gas turbine is vital for achieving the overall effective condition monitoring. This however poses challenges in terms of both efficacy and efficiency for conventional time series models like recurrent neural networks (RNN), since they run in a sequential manner. To this end, this paper introduces a novel parallel probabilistic time series prediction model. Particularly, the proposed model leverages the parallelism of temporal convolutional neural network (TCN) and exploits the power of latent space in the Bayesian framework. Besides, a multi-scale feature extraction strategy based on dense connections and a lagged observation strategy are presented to improve the model performance. In the comparative study against state-of-the art competitors on four classical system identification benchmarks, the proposed model significantly reduces the training time, while consistently achieving highly accurate predictions and providing appropriate uncertainty quantification. Thereafter, the proposed model is adopted to build a systematic workflow for the fast yet accurate long-term forecasting of the temperature of blade channel of gas turbine. The comprehensive results again highlight the superiority of the proposed model in terms of both the prediction quality and the training efficiency.

Improving the control of pharmaceutical compounds in activated sludge wastewater treatment plants: Key operating conditions and monitoring parameters

The control of pharmaceutical compounds (PhCs) in urban wastewater treatment plants (WWTPs) is an issue of health and environmental relevance, expected to become mandatory in the European Union and beyond, but studies at full scale are still scarce. A long-term (2.5-year) full-scale study addressing practical strategies for controlling PhCs was conducted in two activated sludge WWTPs with nitrification (BEI with A2O process; FNW with oxidation ditch). The results showed similar removal patterns in both plants – some of the most abundant influent PhCs were highly removed, others presented intermediate and variable removals, and some were recalcitrant. These behaviours validated a 4-class (A-D) PhC framework (based on biodegradation (kbio) and solid–water distribution (Kd)) for interpreting and predicting PhC removal, particularly sensitive to kbio, with a turning point at 1 L/(gSS.d). A statistical analysis conducted to unveil the key operation variables showed T254 is a good indicator of PhC removal and confirmed that favouring conditions for nitrifiers to grow is an operational strategy to improve PhC control. In BEI, F/M (food to microorganisms ratio) showed the most consistent role (over the sludge retention time), values below 0.08 d−1 being associated with effluent PhC concentrations 26–45 % lower. In FNW, the nitrification role was statistically expressed by the alkalinity reduction, values above 40 % (i.e., Nt-removals >80 %) being associated with effluent PhC concentrations 23–61 % lower.

Characterization and identification of chemical components in traditional Chinese medicine Psoraleae Fructus based on UHPLC-Q-TOF-MS

This study was designed to comprehensively characterize and identify the chemical components in traditional Chinese medicine Psoraleae Fructus by establishing an ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS) method in combination with in-house library. The chromatographic separation conditions(stationary phase, column temperature, mobile phase, and elution gradient) and key MS monitoring parameters(capillary voltage, nozzle voltage, and fragmentor) were sequentially optimized via single-factor experiments. A BEH C_(18) column(2.1 mm×100 mm, 1.7 μm) was finally adopted, with the mobile phase consisting of 0.1% formic acid in water(A) and acetonitrile(B) at the flow rate of 0.4 mL·min~(-1) and column temperature of 30 ℃. Auto MS/MS was utilized for data acquisition in both positive and negative ion modes. By comparison with reference compounds, analysis of the MS~2 fragments, in-house library retrieval and literature research, 83 compounds were identified or tentatively characterized from Psoraleae Fructus, including 58 flavonoids, 11 coumarins, 4 terpenoid phenols, and 10 others. Sixteen of them were identified by comparison with reference compounds, and ten compounds may have not been reported from Psoraleae Fructus. This study achieved a rapid qualitative analysis on the chemical components in Psoraleae Fructus, which provided useful reference for elucidating its material basis and promoting the quality control.

Normalized Temperature Drought Index (NTDI) for Soil Moisture Monitoring Using MODIS and Landsat-8 Data

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales.

Altered Pharmacokinetics Parameters of Vancomycin in Patients with Hematological Malignancy with Febrile Neutropenia, a Bayesian Software Estimation.

The pharmacokinetics of vancomycin vary significantly between specific groups of patients, such as critically ill patients and patients with hematological malignancy (HM) with febrile neutropenia (FN). Recent evidence suggests that the use of the usual standard dose of antibiotics in patients with FN may not offer adequate exposure due to pharmacokinetic variability (PK). Therefore, the purpose of this study is to assess the effect of FN on AUC0-24 as a key parameter for vancomycin monitoring, as well as to determine which vancomycin PK parameters are affected by the presence of FN using Bayesian software PrecisePK in HM with FN. This study was carried out in King Abdulaziz Medical City. All adult patients who were admitted to the Princess Norah Oncology Center PNOC between 1 January and 2017 and 31 December 2020, hospitalized and received vancomycin with a steady-state trough concentration measured before the fourth dose, were included. During the trial period, 297 patients received vancomycin during their stay at the oncology center, 217 of them meeting the inclusion criteria. Pharmacokinetic parameters were estimated for the neutropenic and non-FN patients using the precise PK Bayesian platform. The result showed that there was a significant difference (p < 0.05) in vancomycin clearance Clvan, the volume of distribution at a steady-state Vdss, the volume of distribution for peripheral compartment Vdp, half-life for the elimination phase t½β, and the first-order rate constant for the elimination process β in FN compared to non-FN patients. Furthermore, AUC0-24 was lower for FN patients compared to non-FN patients, p < 0.05. FN has a significant effect on the PK parameters of vancomycin and AUC0-24, which may require specific consideration during the treatment initiation.

Paper sensor-based method using a portable 3D-printed platform and smartphone-assisted colorimetric detection for ammonia and sulfide monitoring in anaerobic digesters and wastewater

A simple and low-cost field-deployable method using paper sensors in combination with a 3D-printed portable platform to simultaneous determination of ammonia and sulfide has been developed. Ammonia and sulfide are harmful pollutants for the environment and important toxicants inhibiting anaerobic digestion, therefore, are key monitoring parameters during wastewater treatment. The sample preparation system consisted of a well microplate and sensor paper discs placed inside the microplate sealing system. Gas pervaporation and diffusion of ammonia and hydrogen sulfide across a Teflon membrane onto a paper discs, impregnated with bromothymol blue and methylene blue assay reagents, allowed the ammonia and sulfide determination, respectively. The detection system consisted in a 3D-printed photographic lightbox with LED plates and a smartphone. A code was developed in ImageJ for a fast colorimetric measurement of the digital images. Under optimal conditions, limits of detection of 1.3 and 1.7 mg L-1 and linear concentration ranges between 5 and 50 mg L-1 and 5–45 mg L-1, were obtained for ammonia and sulfide, respectively. The analysis of an anaerobic digester and wastewater samples revealed good precisions (RSDs < 10 %) and recoveries (91–105 %). This methodology shows advantages such as a high precision, a simple paper discs preparation and a reusable system.

Monitoring the effects of field exposure of acetamiprid to honey bee colonies in Eucalyptus monoculture plantations

Eucalyptus plantations occupy 26 % of Portuguese forested areas. Its flowers constitute important sources for bees and beekeepers take advantage of this and keep their honey bee colonies within or near the plantations for honey production. Nonetheless, these plantations are susceptible to pests, such as the eucalyptus weevil Gonipterus platensis. To control this weevil, some plantations must be treated with pesticides, which might harm non-target organisms. This study aimed to perform a multifactorial assessment of the health status and development of Apis mellifera iberiensis colonies in two similar landscape windows dominated by Eucalyptus globulus plantations - one used as control and the other with insecticide treatment. In each of the two selected areas, an apiary with five hives was installed and monitored before and after a single application of the insecticide acetamiprid (40 g a.i./ha). Colony health and development, resources use, and pesticide residues accumulation were measured. The results showed that the application of acetamiprid in this area did not alter the health status and development of the colonies. This can be explained by the low levels of residues of acetamiprid detected only in pollen and bee bread samples, ~52 fold lower than the sublethal effect threshold. This could be attributed to the low offer of resources during and after the application event and within the application area, with the consequent foraging outside the sprayed area during that period. Since exposure to pesticides in such complex landscapes seems to be dependent on the spatial and temporal distribution of resources, we highlight some key monitoring parameters and tools that are able to provide reliable information on colony development and use of resources. These tools can be easily applied and can provide a better decision-taking of pesticide application in intensive production systems to decrease the risk of exposure for honey bees.

Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.

Experimental measurement of the powder flow velocity in a three-port coaxial laser metal deposition nozzle by high-speed imaging

Additive manufacturing has been successfully used as a new technology to produce complex components with improved properties compared with the traditional manufacturing process. However, the powder used as feedstock requires a homogeneous powder mass flow. Velocity and amount of powders fed into the molten pool are crucial to obtain reproducible deposits (tracks). Therefore, monitoring of key parameters in the laser metal deposition (LMD) process is of high importance to obtain repeatability during either repair or printing components. In order to evaluate LMD relevant parameters, a new particle velocity measurement algorithm is proposed. The experimental tests were digitized with a high-speed camera at 8 kHz. Spherical powder of maraging steel with a close size distribution of 90–150 μm has been used as powder feedstock. The algorithm is composed of three modules: the preprocessing of the images, the calculation of the displacement vectors, and the validation and adjustment of the speeds. The sensitivity of the algorithm was validated using synthetic images, showing good performance at noise rates close to 40 dbW. The uncertainty obtained is 0.62m/s, which was calculated by comparing the results obtained against numerical simulations. The proposed method is specific to additive manufacturing processes using metallic powders as feedstock.

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%.

Research on Submarine landslide monitoring and early warning system

Monitoring and early warning of submarine landslides could provide instant predictions for landslides, which is to avoid the destructive damage of submarine facilities such as pipelines and optical cable, etc effectively. However, researches on submarine landslide focus on numerical simulation and laboratory test, lacking support of in-situ observation data. This paper established the submarine landslide monitoring and early warning system by combining real-time monitoring data with web network platform and database technique. Based on the computational analysis of key monitoring parameters in the process of seabed deformation and sliding, the system has realized the accurate prediction and early warning of submarine landslides. The system has been applied to the submarine landslide monitoring in Zhoushan sea area, Zhejiang province, China, which has ensured the safety of offshore platforms and submarine projects in this area. The establishment of this system provides a new idea and method for submarine landslide warning.

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with R2 &gt; 0.73, root mean squared error (RMSE) &lt; 0.037 mg/L in Lake Okeechobee and R2 &gt; 0.73, RMSE &lt; 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.

Misoperation monitoring and early warning during startup and shutdown of petrochemical units

Most petrochemical units run under extreme conditions, such as high temperatures, pressures, and speeds. Consequently, the equipment operators may commit errors because the startup and shutdown processes usually involve complicated operation steps; moreover, the operators may lack experience in handling abnormal situations. Misoperation can lead to accidents, including fires and explosions. Thus, risk analysis for process operations and the development of preventive measures have become an effective means of avoiding misoperation-related accidents. However, it is challenging to ensure the comprehensiveness of risk-analysis results. In this paper, we present a method for misoperation monitoring and early warning in the startup and shutdown processes of petrochemical units. The mechanisms of misoperation occurrence are summarized based on investigations of serious accidents in the recent past. Knowledge regarding the mechanisms of misoperation is crucial for the risk analysis of petrochemical units. The potential risk information, such as causes, adverse consequences, key monitoring parameters, and prevention control solutions, should be acquired and be employed to construct an early-warning knowledge database. Furthermore, misoperation judgment rules need to be formulated to identify misoperations. The data obtained from the monitoring module, misoperation judgment rules, and analysis results can aid in developing schemes to avoid possible abnormal situations. This paper reports a misoperation monitoring and early-warning system for a hydrogenation unit. As demonstrated, conducting risk analysis to determine the potential operational risks and formulating misoperation judgment rules to analyze the process data are essential for enabling early warning. The application of this method will contribute to operational guidance, economic loss reduction, and accident avoidance.

Redox potential as a key parameter for monitoring and optimization of xylose fermentation with yeast Spathaspora passalidarum under limited-oxygen conditions.

The determination of optimum values of volumetric oxygen transfer coefficient (kLa) for Spathaspora passalidarum is an important aspect for the optimization of ethanol production from pentoses since oxygen plays a key role on yeast metabolism. By studying the fermentation of a xylose and glucose mixture, the highest ethanol volumetric productivity was achieved at a kLa of 45h-1 (1.12gethanol L-1h-1), reducing the fermentation time to half when compared to other oxygen-limiting conditions that were considered optimum for other native strains, besides increasing xylose consumption rates. The high cell density fermentation showed to be a good strategy to be applied in industrial processes with S. passalidarum, enabling the complete exhaustion of a high initial substrate concentration (90gL-1) in less than 24h, with a final ethanol titer of 28.61 (± 0.42) gL-1. By performing a detailed investigation on oxidation-reduction potential (ORP), it was possible to conclude that the highest ethanol formation rates were registered at oxireduction potential values around - 100mV, becoming an important parameter to be controlled when oxygen-limiting conditions are applied in industrial fermentations. The oxygen availability also affected the activity of enzyme XR and its preference for NADH or NADPH, directly affecting the activity of enzyme XDH and the redox imbalance on the xylose pathway. In addition, respirometric parameters were determined for the yeast S. passalidarum under an aerobic growth condition.

Monitoring of water quality in a shrimp farm using a FANET

This paper develops an architecture for flying ad-hoc networks (FANETs) to enable monitoring of water quality in a shrimp farm. Firstly, the key monitoring parameters for the characterization of water quality are highlighted and their desired operational ranges are summarized. These parameters directly influence shrimp survival and healthy growth. Based on the considered sensing modality, a reference architecture for implementing a cost-effective FANET based mobile sensing platform is developed. The controlled mobility of the platform is harnessed to increase the spatial monitoring resolution without the need for extensive infrastructure deployment. The proposed solution will be offered to shrimp farmers in the Mexican state of Colima once the laboratory trials are concluded.

Regional scale soil moisture content estimation based on multi-source remote sensing parameters

ABSTRACT Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized difference index (NDI) ((B 769~797 – B 848~881/B 769~797 + B 848~881); (B 842 – B 740/B 842 + B 740)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R 2) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R 2 = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R 2 values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring.

Biogas productivity of anaerobic digestion process is governed by a core bacterial microbiota

Anaerobic digestion (AD) has been commercially operated worldwide in full scale as a resource recovery technology underpinning a circular economy. However, problems such as a long start-up time, or system instability, have been reported in response to operational shocks. These issues are usually linked to the dynamics of the functional microbiota in AD. Exploring the microbiota-functionality nexus (MFN) could be pivotal to understand the reasons behind these difficulties, and hence improving AD performance. Here we present a systematic MFN study based on 138 samples taken from 20 well-profiled lab-scale AD reactors operated for up to two years. All the reactors were operated in the same lab within the same period of time using the same methodology to harvest physio-chemical and molecular data, including key monitoring parameters, qPCR, and 16S sequencing results. The results showed a core bacterial microbiota prevailing in all reactor types, including Bacillus, Clostridium, Bacteroides, Eubacterium, Cytophaga, Anaerophaga, and Syntrophomonas, while various methanogens dominated different communities due to different inocula origins, reactor temperatures, or salinity levels. This core bacterial microbiota well correlated with biogas production (Pearson correlation coefficient of 0.481, p < 0.0001). Such strong correlation was even comparable to that between the biogas production and the methanogenic 16S rRNA gene content (Pearson correlation coefficient of 0.481, p < 0.0001). The results indicated that AD performance only modestly correlated with microbial diversity, a key governing factor. AD microbiota was neither functionally redundant nor plastic, and a high variety in communities can exhibit a strong difference in reactor performance. Our study demonstrates the importance of a core bacterial microbiota in AD and supports inspiring considerations for design, bioaugmentation, and operational strategies of AD reactors in the future.

Digital Approach to Rotational Speed Measurement Using an Electrostatic Sensor.

In industrial production processes, rotational speed is a key parameter for equipment condition monitoring and fault diagnosis. To achieve rotational speed measurement of rotational equipment under a condition of high temperature and heavy dust, this article proposes a digital approach using an electrostatic sensor. The proposed method utilizes a strip of a predetermined material stuck on the rotational shaft which will accumulate a charge because of the relative motion with the air. Then an electrostatic sensor mounted near the strip is employed to obtain the fluctuating signal related to the rotation of the charged strip. Via a signal conversion circuit, a square wave, the frequency of which equals that of the rotation shaft can be obtained. Having the square wave, the M/T method and T method are adopted to work out the rotational speed. Experiments were conducted on a laboratory-scale test rig to compare the proposed method with the auto-correlation method. The largest relative errors of the auto-correlation method with the sampling rate of 2 ksps, 5 ksps are 3.2% and 1.3%, respectively. The relative errors using digital approaches are both within ±4‰. The linearity of the digital approach combined with the M/T method or T method is also superior to that of the auto-correlation method. The performance of the standard deviations and response speed was also compared and analyzed to show the priority of the digital approach.

A Coupled Model of Two-Phase Fluid Flow and Heat Transfer to Transient Temperature Distribution and Seepage Characteristics for Water-Flooding Production Well with Multiple Pay Zones

Temperature is one of the most prominent factors affecting production operations, predicting the accurate wellbore-formation temperature in a water-flooding production well is of great importance for multiple applications. In this paper, an improved coupled model of oil–water two-phase fluid flow and heat transfer was developed to investigate the transient temperature behavior for a producing well with multiple pay zones. Firstly, a novel method was derived to simulate the water saturation and the water breakthrough time (WBT) for tubing, which are key monitoring parameters in the process of water flooding. Then, we incorporated water saturation and an equation set for immiscible displacement to calculate the seepage velocity and the pressure of the two-phase fluid in the pay zones. Next, the upward seepage velocity of the tubing fluid change with depth was focused on, and the proper coupled initial and boundary conditions are presented at the interfaces, therewith the implicit finite difference method was used to compute the transient temperature with the input of the seepage characteristics for the reservoirs. Meanwhile, the validity of the proposed model has been verified by the typical model. Finally, a sensitivity analysis delineated that the production rate and the production time had a significant impact on the tubing fluid temperature. The overburden was hotter with a lower volumetric heat capacity or a higher thermal conductivity. In addition, the sensitivity of the porosity and the irreducible water saturation to formation temperature was significantly different before and after the WBT. The coupled model presented herein helps to advance the transient seepage characteristics analysis of pay zones, the precise temperature prediction is very useful for reservoir characterization and production analysis purposes and provides insight for designing the exploitation scheme in deep reservoirs and geothermal resources.

Time and Energy Savings in Leak Detection in WSN-Based Water Pipelines: A Novel Parametric Optimization-Based Approach

This paper presents a novel optimization algorithm for monitoring a complex water pipeline using Wireless Sensor Networks (WSN), in order to solve the trade-off between a timely and accurate detection of a leak, and an efficient utilization of the energy at the WSN’s nodes aimed at prolonging the WSN’s lifetime. The scheme relies on using vibration sensors of different sensitivities to detect vibrations due to a leak, and on exploiting duty-cycling, hierarchical adaptive sampling and wavelet-based signal compression, in order to reduce sensing, computation and communication energies. Given the constraints of a maximum allowable sensor energy, a limited time to detect a leak after it occurs, and an acceptable percentage of signal distortion due to compression, a new optimization-based backtracking learning algorithm is developed here that solves for the values of various monitoring parameters such that it satisfies all the given constraints. Developing such an optimization algorithm has also required performing a sensitivity analysis, i.e. investigating the effect of changing the key monitoring parameters on the performance of leak detection and energy consumption. Simulation results for various cases successfully demonstrate the effectiveness of the algorithm while supporting the prediction of the sensitivity analysis.