Determination Of Parameters Research Articles

Defect Recognition Method for Magnetic Leakage Detection in Oil and Gas Steel Pipes Based on Improved Neural Networks

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming non-uniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.

Multi-modal Battle Royale optimizer

AbstractMultimodal optimization poses a challenging problem in the field of optimization as it entails the discovery of multiple local and global optima, unlike unimodal optimization, which seeks a single global solution. In recent years, the significance of addressing multimodal optimization challenges has grown due to the real-world complexity of many problems. While numerous optimization methods are available for unimodal problems, multimodal optimization techniques have garnered increased attention. However, these approaches often grapple with a common issue: the determination of the niching parameter, necessitating prior knowledge of the problem space. This paper introduces a novel multimodal optimization approach that circumvents the need for prior problem space knowledge and avoids the challenge of predefining the niching parameter. Building upon the Battle Royal Optimization (BRO) algorithm, this extended version formulates a multimodal solution by utilizing Coulomb's law to identify suitable neighbors. The incorporation of Coulomb's law serves the dual purpose of identifying potential local and global optima based on fitness values and establishing optimal distances from solution candidates. A comparison study was done between the MBRO and seven well-known multimodal optimization algorithms using 14 benchmark problems from the CEC 2013 and CEC 2015 competitions to see how well it worked. The experimental results underscore MBRO's proficiency in successfully identifying most, if not all, local and global optima, positioning it as a superior solution when compared to its competitors.

Parameters Affecting the Explosion Characteristics of Hybrid Mixtures Arising from the Use of Alternative Energy Sources

Explosions of hybrid mixtures are an interesting theoretical and experimental problem in explosion sciences, because they combine the physicochemical properties of flammable gases and dusts. A hybrid mixture is composed of at least two substances in two or more states. The influence of the common presence of flammable gas on the explosiveness parameters of the combustible dust itself is proven. In this study, we present the effect of higher initiation temperatures, different initial sources of initiation with different energies, and the effect of the volume of explosion chambers on the explosions of hybrid mixtures arising from the use of alternative energy sources. The experiments were carried out in 20 L and 1.00 m3 explosion chambers (according to EN 14034-1+A1:2011–EN 14034-4+A1:2011). The accredited method of the Energy Research Centre, VSB-TU Ostrava, for tests was used. The goal is to approximate the behaviour of these systems under different initiation conditions so that it is possible to avoid excessively conservative or overly optimistic results, which then affect the determination of explosion parameters for practical use. It was found that the volume of the explosion chambers in combination with the used initiation source has a fundamental influence on the course of the explosion characteristics.

Stochastic inversion of soil dynamic parameters based on non-intrusive data

The prediction and evaluation of environmental vibrations generated by traffic loads have been playing more and more important role during the planning and designing of high-quality traffic system. From the traffic source to sensitive building, the subsoil is the one of key propagation media, so the determination of soil dynamic parameters is very crucial in studying the transmitting and attenuation of environmental vibration. Due to the spatial variability of natural soil layers, the soil dynamic parameters are actually not certain but probabilistic with stochastic characteristics. However, most current inversion methods of soil parameters cannot consider the uncertainty. In this paper, an efficient inversion method is proposed to obtain the random distribution of soil dynamic parameters based on the combination of field experiment, forward soil modelling, and Bayesian theory. Take the shear modulus and damping ratio of subsoil as the typical parameters, the experimental dispersion curve and spatial attenuation curve can be obtained by field experiment, while the corresponding theoretical ones can be calculated by the thin-layered soil model with perfectly matched layer. The combining of experimental and theoretical curves generates the likelihood function. A prior probability distribution model is firstly established, and subsequently the posterior probability distribution model is deduced by genetic algorithm, Monte Carlo-Markov chain algorithm, and Bayesian theory. In order to verify the proposed inversion method, an on-site experiment based on Multichannel Analysis of Surface Waves(MASW) was conducted in an open field in Beijing. The results show the mean values of inversed soil parameters were close to the determined parameters provided by geological exploration. Compared to the traditional determined inversion, the proposed stochastic inversion method can consider the variety of soil parameters within small depth by providing probability distribution, mean and root-mean-square error, which has greater applicability in the further probabilistic prediction of environmental vibration.

Contactless assessment of heart rate in neonates within a clinical environment using imaging photoplethysmography.

In neonatology, the accurate determination of vital parameters plays a pivotal role in monitoring critically ill newborns and premature infants, as well as aiding in disease diagnosis. In response to the limitations associated with contact-based measurement methods, substantial efforts have been directed toward developing contactless measurement techniques, particularly over the past decade. Building upon the insights gained from our pilot study, we realized a new investigation to assess the precision of our imaging photoplethysmography-based system within a clinical environment of the neonatal intermediate care unit. We conducted measurements in 20 preterm infants or newborns requiring therapeutic interventions. As a point of reference, we employed a conventional pulse oximeter. To analytically predict measurement artifacts, we analyzed the potential influence of confounding factors, such as motion artifacts, illumination fluctuations (under- and overexposure), and loss of region of interest prior to heart rate evaluation. This reduced the amount of data we evaluated for heart rate to 56.1% of its original volume. In artifact-free time segments, the mean difference between the pulse oximetry and the imaging photoplethysmography-based system for 1 s sampling intervals resulted in -0.2 bpm (95% CI -0.8 to 0.4, LOA ± 12.2). For the clinical standard of 8 s averaging time, the mean difference resulted in -0.09 bpm (95% CI -0.7 to 0.6, LOA ± 10.1). These results match the medical standards. While further research is needed to increase the range of measurable vital parameters and more diverse patient collectives need to be considered in the future, we could demonstrate very high accuracy for non-contact heart rate measurement in newborn infants in the clinical setting, provided artifacts are excluded. In particular, performing a priori signal assessment helps make clinical measurements safer by identifying unreliable readings.

Experimental determination of hypoplastic parameters and cyclic numerical analysis for railway bridge backfills

AbstractLong integral bridges experience an enhanced cyclic soil structure interaction with their granular backfills, especially due to seasonal thermal loading. For numerical modelling of this interaction behaviour under cyclic loading, it is important to employ a suitable constitutive model and calibrate it thoroughly. However, up to the present, experimental data and calibrated soil models for this purpose with focus on typical well-graded coarse-grained bridge backfill materials are rarely available in the literature. Therefore, one aim of this paper is to present results of a comprehensive cyclic laboratory testing programme on highly compacted gravel backfill material. Based on this, a hypoplastic constitutive model with intergranular strain extension for small strain and cyclic behaviour is calibrated and evaluated against the experimental test data. The soil model’s abilities and limitations are discussed at element test level. In addition, cyclic FE analyses of an integral bridge are conducted with several hypoplastic parameter sets from the literature and compared to the calibrated gravel backfill material. The investigation highlights that poorly-graded sands show significantly smaller cyclic earth pressures compared to well-graded gravels intended for the backfilling of a bridge. The soil structure interaction behaviour is clearly governed by the general soil model stiffness, including the small strain stiffness.

Pressure monitoring and deformation analysis of a brine-filled salt cavern - A case study of Jianghan, China

Deformation stability plays a crucial role in the construction and operation of hydrocarbon storage salt caverns. Monitoring and analyzing wellhead pressure data under closed well conditions provide valuable insights into the evaluation of cavern stability. In this study, an analysis of five factors influencing internal brine pressure was conducted, which revealed that salt creep and brine thermal expansion contribute to an increase in internal brine pressure, while brine penetration, salt dissolution and cavern compressibility lead to a decrease. The atmospheric pressure, surface temperature, and earth tides are the other factors influencing wellhead pressure fluctuations. Through the analysis of wellhead pressure data from brine-filled salt cavern under closed conditions, the creep parameters of salt formations were calculated using the Norton-Hoff creep model. To further understand the behavior of brine-filled salt cavern, numerical simulations were conducted to analyze the brine temperature distribution and deformation stability. The volumetric average temperature of the brine exhibits a nonlinear increase over time, characterized by a rapid rise in the initial stage followed by a slower rate of change. Finally, the brine temperature stabilizes and converges toward the formation temperature. From deformation analysis, it was observed that the closed condition is more favorable for the maintenance of cavern stability in comparison to the open condition. The subsidence of the cavern roof is particularly sensitive and requires careful attention, especially for flat-roof salt caverns. Under closed conditions, it is crucial to control the brine pressure decrease rate and prevent excessively high brine pressure to ensure the mechanical and penetration stability of the brine-filled salt cavern. These findings provide valuable insights into the factors influencing internal brine pressure, the determination of creep parameters, the distribution of brine temperature, and the deformation stability of brine-filled salt caverns.

Determination of Wax Deposition Rate Model of Blended Oils with Different Blending Ratios

Blending with light oil is a commonly used and reliable method of crude oil transportation, and the blending ratio is a crucial operating parameter in determining the safe and efficient operation of the pipeline. In this paper, in-house flow and deposition experiments are used to evaluate the flow and deposition characteristics of crude oils with varying blending ratios. The results show that (1) blending with light oil basically does not affect the shape of the DSC curve of crude oil; (2) blending with light oil will not eliminate the thermal treatment effect, and the mixed oil flowability still remains highly dependent on the thermal treatment temperature; (3) blending with light oil can greatly decrease the abnormal point and oil viscosity, in which the low-temperature viscosity decreases more significantly; and (4) a wax deposition model of mixed oil is obtained through the fitting of Huang’s model, where the blending ratio is a crucial factor in the determination of the model parameters k, m, and n.

Effects of 2,4-dichlorophenol on non–specific immunity, histopathological lesions, and redox balance in African Catfish, clarias gariepinus (Burchell, 1822)

ABSTRACT The toxic effects of 2, 4-dichlorophenol (2, 4-DCP) on aquatic organisms are well-established; however, the details regarding the mechanisms underlying the toxicity, especially immunotoxicity are poorly understood. Consequently, the aim of this study was to investigate the histopathologic, oxidative stress and immunotoxic effects attributed to exposure to sublethal concentrations of 2,4-DCP in the African catfish, Clarias gariepinus. Juvenile C. gariepinus were exposed to 0.4, 0.8, or 1.6 mg/L 2, 4-DCP for 28 days after which blood and head kidney were extracted for the determination of various nonspecific innate immune parameters while the liver was excised for histopathology examination and measurement of oxidative stress biomarkers. Control fish were maintained in water spiked 10 µL/L ethanol, representing the solvent control. A significant increase was noted in the activities of lactate dehydrogenase and superoxide dismutase as well as in levels of lipid peroxidation and DNA fragmentation in a dose-dependent manner, with higher adverse effects observed at the highest concentration tested (1.6 mg/L). The total white blood cells (WBC) count was significantly elevated in fish exposed to 2,4-DCP compared to control. Myeloperoxidase content was decreased significantly in fish exposed to 2,4-DCP especially at the highest concentration (1.6 mg/L) compared to controls. The respiratory burst activity did not differ markedly amongst groups. Histopathological lesions noted included edema, leucocyte infiltration, and depletion of hemopoietic tissue in the head kidney of exposed fish. There was significant upregulation in the mRNA expression of tumor necrosis factor (TNF-α) and heat shock protein 70 (HSP 70) but downregulation of major histocompatibility complex 2 (MHC 2) in exposed fish. Data demonstrated that exposure to 2,4-DCP resulted in histopathological lesions, oxidative stress, and compromised immune system in C. gariepinus.

Unemployment Rate Prediction Using a Hybrid Model of Recurrent Neural Networks and Genetic Algorithms

Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to make short-term estimates, assess economic health, and make informed monetary policy decisions. This paper proposes the innovative GA-LSTM method, which fuses an LSTM neural network with a genetic algorithm to address challenges in unemployment prediction. Effective parameter determination in recurrent neural networks is crucial and a well-known challenge. The research uses the LSTM neural network to overcome complexities and nonlinearities in unemployment predictions, complementing it with a genetic algorithm to optimize the parameters. The central objective is to evaluate recurrent neural network models by comparing them with GA-LSTM to identify the most appropriate model for predicting unemployment in Ecuador using monthly data collected by various organizations. The results demonstrate that the hybrid GA-LSTM model outperforms traditional approaches, such as BiLSTM and GRU, on various performance metrics. This finding suggests that the combination of the predictive power of LSTM with the optimization capacity of the genetic algorithm offers a robust and effective solution to address the complexity of predicting unemployment in Ecuador.

Reliability-oriented segmentation of sublayers in geologically uncertain substrate: A case study of the Żelazny Most TSF

In this study, a new approach is proposed for the stability analysis of tailings storage facility (TSF) dams. Given the considerable scale of such objects, te correct identification of geotechnical layers for computational models and the determination of reliable parameters can present significant challenges. Therefore, the investigation focuses on distinguishing additional sublayers within a geological substratum to enhance the credibility of stability assessment. The procedure of sublayer separation is directly based on soil parameters derived from geotechnical tests and it aims at reducing the variability in the parameters of individual sublayers. Specifically, the segmentation technique known as the Gaussian mixture model (GMM) is employed for this purpose. Subsequently, the spatial arrangement of sublayers within the finite element model domain is identified using the kriging technique. Utilizing Monte Carlo simulations statistical distribution of factor of safety (FOS) is obtained for the structure. Finally, the stability is assessed using the Cornell reliability index. The application of this developed methodology is demonstrated through a case study of the Żelazny Most TSF. Additional sublayers are identified in Neogene clays constituting the dominant layer beneath the facility. In total, six different segmentation variants are proposed and their performance is evaluated. The results show that the segmentation of additional sublayers produces a clear reduction in the scatter of the FOS. Consequently, the estimated simplified reliability index surpasses that of the case, which does not involve the separation of additional layers.

Globin: A spectropolarimetric inversion code for the coupled inference of atomic line parameters

The reliability of physical parameters describing the solar atmosphere inferred from observed spectral line profiles depends on the accuracy of the involved atomic parameters. For many transitions, atomic data, such as the oscillator strength ( ) and the central wavelength of the line, are poorly constrained or even unknown. We present and test a new inversion method that infers atomic line parameters and the height stratification of the atmospheric parameters from spatially resolved spectropolarimetric observations of the Sun. This method is implemented in the new inversion code The new method employs a global minimization algorithm enabling the coupling of inversion parameters common to all pixels, such as the atomic parameters of the observed spectral lines. At the same time, it permits the optimum atmospheric parameters to be retrieved individually for each spatial pixel. The uniqueness of this method lies in its ability to retrieve reliable atomic parameters even for heavily blended spectral lines. We tested the method by applying it to a set of 18 blended spectral lines between 4015\ and 4017\ synthesized from a 3D magnetohydrodynamic simulation containing a sunspot and the quiet Sun region around it. The results were then compared with a previously used inversion method where atomic parameters were determined for every pixel independently (pixel-by-pixel method). For the same spectral region, we also inferred the atomic parameters from the synthesized spatially averaged disc-centre spectrum of the quiet-sun. The new method was able to retrieve the values of all lines to an accuracy of 0.004\,dex, while the pixel-by-pixel method retrieved the same parameter to an accuracy of only 0.025\,dex. The largest differences between the two methods are evident for the heavily blended lines, with the former method performing better than the latter. In addition, the new method is also able to infer reliable atmospheric parameters in all the inverted pixels by successfully disentangling the degeneracies between the atomic and atmospheric parameters. The new method is well suited for the reliable determination of both atomic and atmospheric parameters and works well on all spectral lines, including those that are weak and/or severely blended. This is of high relevance, especially for the analysis of observations of spectral regions with a very high density of spectral lines. An example includes the future near-ultraviolet spectropolarimetric observations of the Sunrise iii stratospheric balloon mission.

Satellite laser ranging to BeiDou-3 satellites: initial performance and contribution to orbit model improvement

In January 2023, the International Laser Ranging Service (ILRS) approved the tracking of 20 additional BeiDou-3 Medium Earth Orbit (BDS-3 MEO) satellites, integrating them into the ILRS tracking network. Before that, only 4 BDS-3 MEO satellites had been tracked. BDS satellites employ highly advanced GNSS components and technological solutions; however, microwave-based orbits still contain systematic errors. Satellite Laser Ranging (SLR) tracking is thus crucial for better identification and understanding of orbit modeling issues. Orbit improvements are necessary to consider BDS in future realizations of terrestrial reference frames, supporting the determination of global geodetic parameters and utilizing them for the co-location of GNSS and SLR in space. In this study, we summarize the first 6 months of SLR tracking 24 BDS-3 MEO satellites. The study indicates that the ILRS network effectively executed the request to track the entire BDS-3 MEO constellation. The number of observations is approximately 1300 and 450 for high- and low-priority BDS-3 satellites, respectively, over the 6 months. More than half of the SLR observations to BDS-3 MEO satellites were provided by 5 out of the 24 laser stations, which actively measured GNSS targets. For 14 out of 24 BDS-3 MEO satellites, the standard deviation of SLR residuals is at the level of 19–20 mm, which is comparable with the quality of the state-of-the-art Galileo orbit solutions. However, the SLR validation of the individual satellites revealed that the BDS-3 MEO constellation consists of more ambiguous groups of satellites than originally reported in the official metadata files distributed by the BDS operators. For 8 BDS-3 satellites, the quality of the orbits is noticeably inferior with a standard deviation of SLR residuals above 100 mm. Therefore, improving orbit modeling for BDS-3 MEO satellites remains an urgent challenge for the GNSS community.

Determination of the Kinetic Parameters of Thermal Degradation and Hydrodemetallization of a Mixture of the Heavy Fraction of Low-Temperature Coal Tar and Coal Shale

The laws of thermal degradation of the mixture of the heavy fraction of low-temperature coal tar and coal shale were investigated using dynamic thermogravimetry. The kinetic characteristics of the process were determined using various methods, including the Ozawa–Flynn-Wall, Friedman, non-parametric kinetics and Šesták–Berggren methods. It is shown that coal shale initiated changes in the kinetic parameters and decomposition rate of the heavy fraction of coal tar. It was found that a 13% content of coal shale in the mixture led to the maximum rate of weight loss of the heavy fraction of coal tar. A hydrodemetallization kinetic model of the mixture of the heavy fraction of low-temperature coal tar and coal shale is proposed. The kinetic parameters of the hydrodemetallization process were determined; in addition, the rate constants at various temperatures were estimated. The study shows that the distribution of trace elements in the hydrogenate from the initial mixture and in the hydrogenate from the solid residue was characterized by relatively low values of reaction rate constants. The maximum microelement distribution rate was achieved in the hydrogenate solid residue. Energy indicators of activation processes indicated that hydrodemetallization at low temperatures is advantageous from an energy point of view.

Experimental and theoretical study on high-temperature creep of VT6 titanium alloy under multi-axial loading conditions

In the framework of damage mechanics, we discuss a new mathematical model that describes the kinetics of the stress–strain state and damage accumulation during material degradation by the mechanism of long-term strength under complex multiaxial stress state. An experimental and theoretical technique is proposed for determination of material parameters and scalar constitutive functions for damaged media based on specially set experiments on laboratory specimens. The results of experimental studies and numerical simulations of short-term high-temperature creep of VT6 titanium alloy under uniaxial and multiaxial loading are presented. Numerical results are compared with the data obtained through experiments. Particular attention is paid to simulating the process of unsteady creep for complex deformation modes, accompanied by rotation of main areas of stress, strain and creep strain tensors. It is shown that the developed version of the constitutive relations of the damaged media enables us to describe the processes of unsteady creep and long-term strength of structural alloys under multiaxial stress with the accuracy sufficient for engineering calculations.

Study of the Accuracy Characteristics of the Mathematical Model for Predicting Changes in the Error of the Working Measurement Standard

The accuracy of the forecasting method and its prior estimation when predicting changes in the error of the working measurement standard are one of the key issues. In the course of studying these issues, a number of components of the forecasting error were assessed, and various methods and mathematical models of forecasting were compared. A comparative analysis showed that the studied mathematical model for individual forecasting of changes in the error of a working standard available at «Mathematical model for predicting changes in the value of the critical component of the error of the working measurement standard of the unit of magnitude taking into account prior information» has higher accuracy in comparison with the considered known forecasting methods. The estimates of the parameters of the forecasting function obtained with the application of the model under consideration available at «Determination of parameters for metrological maintenance of measuring instruments by the technical and economic criterion», using the expressions of the transition to forecasting the probability of metrological serviceability of the working measurement standard, specific costs for metrological maintenance, and damage from the use of the working measurement standard in the state of metrological failure, allow to substantially increase the validity of decisions to refine the value of the interval between the certification of the working standard, which is initially established (within the framework of the ITCM).

Event‐triggered adaptive estimated inverse neural network control of uncertain nonlinear systems with unknown hysteresis effects

AbstractThis article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller.

Determination of bioactive compounds and physicochemical param-eters of honey produced in the state of Veracruz, Mexico.

Veracruz occupies one of the first places as a producer of honey. However, there are few studies that address the typing of bioactive compounds and the physicochemical characteristics present in Veracruz honey.
 Objective: Determine the physicochemical and antioxidant parameters, and total phenols (TP) of Veracruz honey.
 Design/methodology/approach: Honey samples were collected at 17 sites in the state of Veracruz, and physicochemical, TP and antioxidant parameters were analyzed.
 Results: The determined physicochemical parameters presented values within the ranges set by NOM-004-SAG/GAN-2018 and the Codex Alimentarius. The color distribution showed the following values: dark (47.6%), amber (19.04%), white (19.04%), and the colors aqua white, light amber and extra light showed values of 4.77% each. Dark honeys presented ~370 μg GAE/mL, compared to the contents shown by light-colored honeys of ~200 μg GAE/mL. Sayula de Aleman honey had the highest antioxidant content with 143 mg TE/g honey. On the other hand, honey from San Pedro Soteapan showed the lowest contents (53 mg TE/g honey).
 Limitations on study/implications: The selection of Veracruz honey apiaries and lack of flora information.
 Findings/conclusions: The honeys presented physicochemical parameters within ranges of national standards. These Veracruz honeys exhibited a range of colors from dark to extra light. A positive correlation was shown between color and TP content. The antioxidant content was dependent on the botanical origin and color of these honeys.

Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration

This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.

Yarrowia lipolytica CMGB32 Biosurfactants Produced Using n-Hexadecane: Developing Strategies for Environmental Remediation

The yeast Yarrowia lipolytica degrades petroleum compounds, including alkanes, via the monoterminal oxidation pathway, the hydrophobic carbon substrate assimilation is mediated by biosurfactants, and extracellular amphiphilic molecules are produced by the yeast cell. This study focuses on the ability of the strain Y. lipolytica CMGB32 to degrade n-hexadecane by producing biosurfactants with high potential for bioremediation. The hydrocarbon-degrading potential of the yeast strain was observed via a 2,6-dichlorophenolindophenol (DCPIP) test in Bushnell–Hass medium with 1% n-hexadecane, and cell hydrophobicity was expressed as microbial adhesion to hydrocarbons (MATH). Biosurfactant production on yeast peptone (YP) with 1% n-hexadecane was estimated after 72 h using the emulsification index (E24%) against toluene. Crude biosurfactant (cell-free broth) stability tests were performed at different temperatures (4 °C, 70 °C) and NaCl concentrations (2–10%). The effects of a biosurfactant on synthetic wastewater remediation comprised the growth curves (OD measurements) of natural heavy metal degrader Rhodotorula mucilaginosa, determination of nutrients (spectrophotometrically), physico-chemical parameters, and removal capacity of lead and cadmium ions (via inductively coupled plasma mass spectrometry—ICP-MS). The antimicrobial and anti-adherence activities of 20 mg/mL and 40 mg/mL of the biosurfactant against pathogenic Candida krusei strains involved growth observations and the crystal violet microtiter method. The DCPIP decolorization occurred after six days, corresponding to the maximum growth phase of the Y. lipolytica culture. After 72 h, the cells presented high hydrophobicity (82.61% MATH) and stable biosurfactant production (E24% 47%). The crude biosurfactant (5%) increased the growth of R. mucilaginosa strains cultivated on synthetic wastewater cultures contaminated with Pb2+ and Cd2+, increased the conductivity and COD (86%) of the samples, and determined Pb2+ (66%) and Cd2+ (42%) ions reduction. The concentrated biosurfactant inhibited C. krusei growth (70%) and biofilm adherence. In conclusion, Y. lipolytica CMGB32 shows important potential for development of biosurfactant-based technologies for the remediation of heavy-metal- and emerging pathogen-contaminated wastewaters.