Pipeline inspection gauges (PIGs) carry out automatic pipeline inspection with nondestructive testing (NDT) technologies like ultrasound, magnetic flux leakage, and eddy current. The ultrasonic straight beam allows technicians to determine the wall thickness of the pipeline through the time of flight diffraction (TOFD), providing the pipeline reconstruction and allowing the detection of several defects like dents or corrosion. If the pipeline is of a long distance, then the inspection process is automatic, and the fluid pressure pushes the PIG through the pipeline system. In this case, the PIG velocity and its axial alignment with the pipeline cannot be controlled. The PIG geometry, the pipeline deformations, and the girth welds cause a continuous chattering when the PIG is running, removing the transducers perpendicularity with the inspection points, which means that some echoes cannot be received. To reduce this problem, we propose a novel method to design a sensor carrier that takes into account the angularity and distance effects to acquire the straight beam echoes. The main advantage of our sensor carrier is that it can be used in concave and convex pipeline sections through geometric adjustments, which ensure that it is in contact with the inner pipe wall. Our improvement of the method is the characterization of the misalignment between the internal wall of the pipeline and the transducer. Later, we analyzed the conditions of the automatic pipeline inspection, the existing recommendations in state-of-the-art technology, and the different mechanical scenarios that may occur. For the mechanical design, we developed all the equations and rules. At the signal processing level, we set a fixed gain in the filtering step to obtain the echoes in a defined distance range without saturating the acquisition channels. For the validation, we compared through the mean squared error (MSE) our sensor carrier in a straight pipe section and a pipe elbow of steel versus other sensor carrier configurations. Finally, we present the design parameters for the development of the sensor carrier for different pipeline diameters.

In this manuscript, we present a redundant data storage system based on NAND flash memory chips for in-line Pipeline Inspection Gauges (PIGs). The system is the next step for a technique that reduces data from 1,024 to 37 bytes by 80 transducers used for straight-beam ultrasonic inspection. Each inspection is costly, because PIGs check pipelines up to 100 Km, collecting data every 3 mm and reaching speeds of 2 m/s. These conditions require that the storage system must be redundant, and able to maintain a minimum data flow, thus avoiding bottlenecks. To achieve this, we analyzed the variables that influence the inspection process in real-time, and we structured our Flash Translation Layer (FTL) to eliminate the latencies generated by the computation of the Error Correcting Codes (ECC) and redundancy bytes. Our controller computes the ECC and redundancy bytes while it transfers the information to the cache register of the selected die in the memory chips. At the hardware level, we interleaved 8 NAND flash chips in a Redundant Array of Independent Disks (RAID) type-6 architecture. We tested the storage system considering the incorrect response of up to 2 chips and ensuring a throughput up to 7.28 MB/s. Finally, we expanded the analysis of the data flow, whereby this system is profitable for different pipeline diameters or compression techniques.

Current hemodialysis treatments can cause adverse effects, many of which are linked to the membranes used in the process. These issues are being addressed through new materials and technologies, making it urgent to establish minimum guidelines for evaluating such membranes. This review proposes standardizing the biological tests and variables to evaluate the performance of new membranes, aiming to replicate hemodialysis conditions closely. The tests were categorized into protein adsorption, protein transmission, platelet adhesion, platelet activation, blood coagulation times, hemolysis, complement activation, and cytotoxicity. For protein adsorption, static tests are recommended as an initial step to rule out membrane adhesion, followed by dynamic tests that must be conducted using a crossflow system (>250 mL/min flow) and a solution mimicking real conditions (BSA, lysozyme, trypsin, pepsin, creatinine, urea, albumin, fibrinogen, and γ-globulin). Protein transmission tests must employ dynamic conditions, using human blood or platelet-rich plasma for a minimum time of 3.5 h. Complement activation should be tested using human blood and ELISA assays to detect C3, C5 TCC, and SC5b-9. Blood coagulation times (APTT, TT, FT, TCT, and TAT) should be measured with platelet-poor and platelet-rich plasma. Hemolysis tests should transition from water bath to continuous mode for at least 3.5 h. Cytotoxicity tests should compare the MTT assay with other methods (Alamar Blue, Lactate Dehydrogenase Assay, Flow Cytometry, or Trypan Blue Exclusion Test) and use different cell types for comprehensive validation. By implementing these minimum biological tests, membrane evaluations would more accurately reflect the real-world applications, ensuring biocompatibility, effectiveness, and efficiency.

Peristaltic pumps represent a fundamental component of hemodialysis machines. They facilitate the transfer of fluids, particularly in the collection and treatment of blood. This study aims to improve pump precision and reliability by reducing steady-state error and optimizing flow consistency, measured in milliliters per minute. A detailed characterization established the relationship between revolutions per minute (RPM) and flow rate (mL/min), with redundant mass and volume measurements supporting accuracy. To model the system’s behavior, two non-linear functions and one linear function were compared, with the polynomial model proving the most accurate and revealing the pump’s inherently non-linear flow behavior. A proportional–integral (PI) controller was then applied, and optimized through step input and non-linear least squares fitting. A key aspect of this study is a comparative validation against a commercial hemodialysis machine, configured identically with the same blood circuit diameter, tubing brand, and filter, in order to ensure equivalency in conditions. Results showed a maximum flow rate error of 0.5296%, highlighting the integration of control and characterization methods that enhance system precision, dependability, and reproducibility—critical factors for ensuring the safety and effectiveness of hemodialysis treatments.

Pollutant emissions into the atmosphere are recognized as a significant problem in fossil fuel combustion. The pollution emission measurement in industrial boilers is difficult and expensive but fundamental for monitoring and controlling. Frequently continuous emissions monitoring (CEM) system is out of service or useless due to obsolescence, high maintenance cost, and so on or simply is not installed. When a system for measuring pollutant emissions is not available, an alternative method must be employed to get the pollutant emission value. According to the black-box model approach, this article describes the nonlinear modeling of NOx emissions from a utility boiler. Bayesian-Gaussian (BG), multilayer perceptron (MLP), and Volterra polynomial basis functions (VPBF) neural networks are developed for model benchmarking. Experimental data from a utility boiler was acquired in order to model definition and evaluation. The models process three boiler variables oxygen excess, fuel mass flow and flue gas recirculation gates for NOx emission estimation. Models with BG show better performance than models with MLP and VPBF for NOx prediction. Implications: The technology to control NOx emissions generated by combustion operates under strict regulations. In order to reduce NOx emissions, theoretical models of NOx generation have been studied extensively, including nitrogen chemistry and the dynamic flow of gas particles which is very complex. The new technology trends would require the continuous measurement of high precision NOx emissions to achieve further reductions in NOx emissions. Currently, NOx emissions are measured by a Continuous Emission Monitoring system, which turns out to be extremely expensive and difficult to maintain, so alternative low-cost solutions are desirable. Our contribution shows how algorithms based on different artificial intelligence techniques are viable and quality alternatives for the measurement of continuous NOx emissions. The NOx emissions models based on IA algorithms are viable alternatives that have versatility and self-tuning capacity due to the fact that they are based on boiler operation parameters which have valuable information few explored nowadays.