Handling Methods Research Articles

Predictive modeling of CO2 capture efficiency using piperazine solutions: a comparative study of white-box algorithms

The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.

Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.

Wrapping method for better fat handling in endoscopic trans-sphenoidal surgery

Background: Fat is commonly used for preventing cerebrospinal fluid (CSF) leakage during endoscopic transsphenoidal surgery (ETSS). However, fat is soft, slippery, and sometimes not easy to handle. The present study aimed to examine the efficacy of our Surgicel® wrapping method, which allows for better fat handling, in preventing the occurrence of CSF leakage among patients undergoing ETSS. Methods: We used fat tissues removed from the abdomen. The fat was cut with scissors into pieces that were approximately 5 mm in size. Surgicel® was also cut into 2.5 cm2. The fat tissues were encased with these Surgicel® squares and slightly moistened with a saline solution. Results: Between January 2023 and August 2024, 34 patients aged 18–86 years (average 54.9 years) underwent ETSS. Among these patients, 20 had pituitary tumors, 6 had Rathke’s cysts, and 8 had other conditions. None of the patients had CSF leakage postoperatively. The use of Surgicel®-wrapped fat during ETSS is better than the use of fat alone. Conclusion: The Surgicel® wrapping method allows for better fat handling during ETSS.

Effectiveness of KMnO4 and activated carbon on the quality and storage properties of mango fruit

Cat Hoa Loc mango (Mangifera indica L.) is a well-known variety in Vietnam because of its distinct taste and aroma. However, it has a short shelf life and can suffer post-harvest losses if not handled and packaged correctly. The demand for fresh Cat Hoa Loc mangoes has been increasing worldwide, and this has led to the development of effective handling methods to extend their shelf life. To address this issue, this study was conducted to evaluate the effectiveness of KMnO4 and activated carbon in preserving the quality and shelf life of Cat Hoa Loc mangoes in Vietnam. The Cat Hoa Loc mango variety in Cao Lanh, Dong Thap province, was chosen for the study. The study used five replications of a completely randomized block design. Six different treatments with KMnO4 and activated carbon (1:1 ratio) were tested, including 0; 4; 8; 12; 16; 20 g/box. Six mangoes were stored in perforated cartons (36x26x9 cm) at room temperature (28-30°C) during the study period. The study evaluated several parameters to assess the quality and shelf life of the mangoes, including weight loss, fruit firmness, browning index, respiration, ethylene release rate, soluble sugar, and vitamin C. The results showed that the quality of the mangoes was extended when treated with 12 g of KMnO4 and activated carbon per box. This treatment resulted in the lowest physiological weight losses, respiration, and ethylene release rate. Furthermore, this treatment showed the highest fruit firmness, soluble sugar, and vitamin C content, as well as the longest shelf life at the end of the storage period.

Impact of Various Washing Protocols on the Mitigation of Escherichia coli Contamination in Raw Salad Vegetables.

Vegetables are an essential component of a balanced diet. The consumption of ready-to-eat foods may lead to the risk of infections and illnesses due to microbial contamination. To mitigate the potential of microbial contamination risks, it is critical to promote safe handling practices among consumers. In this study, our research evaluated the efficacy of different vegetable washing methods, specifically with lettuce, tomato, and cucumber, to establish optimal practices for reducing microbial contamination. This study consisted of two phases. Initially, a survey was distributed to 150 volunteers using snowball sampling to assess everyday vegetable handling and washing methods. The survey's results identified four predominant methods: washing with a 5% vinegar solution for 3 min followed by tap water rinse (37.3% of participants), rinsing with tap water for 1 min (29.3%), washing with a 5% salt solution (vegetable soap) for 3 min followed by a tap water rinse (16.6%), and a 3 min tap water rinse (14%). A minor segment (3.33%) reported not washing their vegetables at all. The survey's findings guided the second phase, which tested the aforementioned washing protocols' effectiveness in reducing Escherichia coli (E. coli) levels on spiked contaminated salad vegetables. The tested vegetables were sterilized using UV light, inoculated with 0.5 McFarland E. coli, and then washed using the four identified methods. After that, E. coli enumeration after washing was performed using 3M™ Petrifilm and the comparison was analyzed via one-way ANOVA. During this study, it was revealed that the cucumbers had the highest E. coli contamination levels in comparison to the lettuce and tomato after washing. Interestingly, by comparing the three washing methods, it was found that washing the vegetables with vinegar proved to be the most effective solution for reducing microbial presence on both lettuce and cucumbers. Notably, the natural smoothness of tomato skin led to no significant differences in contamination levels across washing methods. In summary, vinegar washing effectively reduces microbial contamination from salad vegetables, highlighting the need for informed consumer practices to prevent foodborne outbreaks. This study emphasizes the importance of monitoring contamination sources and using safe washing techniques.

Effect of water to cement ratio on mechanical properties of FRC subjected to elevated temperatures: Experimental and soft computing approaches

The deterioration of concrete is greatly dependent on the cracks and microcracks development owing to loading or environmental influences. An effective step to avoid the spread of cracks and microcracks is employing of different fibers in concrete and the manufacture of fiber reinforced concrete. On the other hand, fire is one of the cases that always threatens engineering structures. Elevated temperatures cause obvious chemical and physical changes that lead to concrete deterioration. In this study, the effect of adding various fiber with different volume contents in concrete mixture with various cement content was evaluated experimentally. Results indicate that spalling was more dominant in mixture containing steel fiber and with higher amount of cement, while there is not any spalling in mixture containing polypropylene (PP) fibers. Moreover, the reduction in tensile strength of fiber-free concrete specimens is less pronounced in mixtures with higher cement content. In addition, the positive performance of PP fibers compared to steel fibers was proved at higher temperatures and cement contents. Furthermore, by conducting a comprehensive and accurate survey, this research pinpoints gaps in the current literature. Moreover, the gathered dataset serves as input for training a machine learning approach known as Group Method of Data Handling (GMDH). The GMDH network demonstrates a satisfactory accuracy in predicting experimental results, with a MSE of 0.0044.

Quality evaluation of the forecasting system for the Polish Timescale by means of the Group Method of Data Handling type neural network

The article presents the results aimed at assessing the effectiveness of the applied forecasting system based on the forecasting procedure using a GMDH (Group Method of Data Handling) neural network for the Polish Timescale (UTC(PL)) implemented on the basis of hydrogen maser (HM). The article also aims to show that the application of the forecasting system to implement the UTC(PL) national timescale makes it possible to achieve a quality of this timescale similar to the best of the best timescales. The results of forecasting using the GMDH neural network for UTC(PL) are presented for two prepared time series (TS1 and TS2), which are additionally compared with the UTC - UTC(k) values for this scale. Very good forecasting quality has been achieved for the analysed cases 5 and 6, both for data prepared according to the TS1 and TS2 time series. This is confirmed by the calculated values of forecast quality measures. The obtained research results show that the developed system allows ensuring the accuracy of the UTC(PL) at the level of best timescales. The presented research results will help to convince some of NMIs (National Metrology Institutes), which are not equipped with caesium fountains, to adopt the UTC(k) steering system.

Liquid Handling Technologies: A Study through Major Discoveries and Advancements

Liquid handling is an essential process in laboratories involving automatically transferring accurate amounts of liquids for various task completions such as sample preparation, pipetting, dilution, and mixing. Automated liquid handling systems (ALHS) help to minimize the errors, complications, and difficulties that are faced with manual methods of liquid handling by efficiently enhancing the accuracy, precision, and reproducibility of experimental workflows and have transformed life sciences aiding research in various fields. Processes like Nuclear Magnetic Resonance (NMR) spectrometry, mass spectrometry, and acoustic droplet ejection with ALHS showcase tremendous potential. Outdated manual machinery is being replaced rapidly with automated tools. We have observed that these laboratory setups are sophisticated and costly. It is challenging to access such technologies, especially in poor or developing countries. At the nanoscale level, handling and evaporation of biomaterials becomes a great issue. Multitudinous devices can combat the cost issue along with utilization of single equipment without the enhancement of add-on modules. Liquid handling devices enhance the quality of experiments contributing to efficient dealing of circumstances like COVID-19. They aid in genomics and proteomics and play a pivotal role in liquid manipulation, drug screening, quantifications, forensic studies and many more. Liquid handling devices with robotic equipment can perform multiple tasks and can assist in a wide range of fields. We anticipate that in the coming years, our laboratories will be furnished with advanced, robotic technologies and cause a revolution in the scientific scenery.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Investigation of a collective food poisoning in the province of Kenitra - Morocco from 2019 to 2023: A study to determine the epidemiological profile and the effectiveness of the management measures implemented

Aim The objective of this study is to examine collective foodborne illnesses (TIAC) reported in the province of Kenitra from 2019 to 2023, with the aim of determining the epidemiological profile and evaluating the effectiveness of the management measures implemented. On August 5, 2019, the survey therefore highlighted 9 cases (response rate of 75%), with an average age of 29 years and 75.5% women. Nausea, abdominal pain, diarrhea and vomiting were the main symptoms. The study revealed a significant correlation between the consumption of fish (75% attack rate), fries (90%) and soda (80%). Likewise, the study conducted on February 1, 2022 revealed a significant correlation between intoxication and consumption of soup (RR=1.4), chicken (RR=2.4), salad (RR= 1.62) and bread (RR=2.4). Rice presents a higher risk (RR=5.1, p=0.05). Levels of staphylococcus aureus were detected during microbiological analyses, exceeding acceptable thresholds. Faced with these findings, monitoring of food safety practices seems necessary. Preventing these outbreaks in the future would be most effective by improving food handling, storage and preparation methods. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 1020-1029

Визначення предикторів діагнозу пацієнтів з ПТСР на основі параметрів одновимірних моделей першого порядку BOLD-сигналів структур мозку та МГУА

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients. Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures. Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling. Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity. Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а0 of the models) and the alteration of these levels in the context of PTSD.

White-Box Machine Learning Models for Accurate Interfacial Tension Prediction in Hydrogen-Brine Mixtures

Abstract The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources. One intrinsic challenge of renewable energy sources is their intermittent nature, which can be addressed by transforming excess energy into hydrogen and storing it safely for future use. To securely store hydrogen underground, a comprehensive knowledge of the interactions between hydrogen and residing fluids is required. Interfacial tension is an important variable influenced by cushion gases such as CO2 and CH4. This research developed explicit correlations for approximating the interfacial tension of hydrogen-brine mixture using two advanced machine learning techniques: gene expression programming and the group method of data handling. The Interfacial tension of hydrogen-brine mixture was considered as heavily influenced by temperature, pressure, water salinity, and average critical temperature of the gas mixture. The results indicated a higher performance of the group method of data handling-based correlation, showing an average absolute relative error of 4.53%. Subsequently, Pearson, Spearman, and Kendall methods assessed the influence of individual input variables on the correlations’ outputs. Analysis showed that temperature and average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension values. Finally, the reliability of the gathered databank and the scope of application for the proposed correlations were verified by the leverage approach by illustrating 97.6% of the gathered data within the valid range of the Williams plot.

E-Waste Management Practices Through the Eyes of Responsible Departments at Malaysian Public Universities

Electronic and electrical waste (E-waste) has been identified as the fastest-growing waste stream in the world. In Malaysia, forecasts indicate that it will soon reach 24.5 million units of e-waste by the year 2025. Today, higher learning institutions also contribute to the rapidly growing threat of e-waste. Besides mobile phones, the usage of Information and Communication Technology (ICT) equipment at universities keeps increasing and contributes to the increased quantity of e-waste, while the handling and disposal methods of e-waste still need to be improved. The purpose of this paper is to examine the current e-waste management system as well as to investigate the critical challenges constraining e-waste management at public universities in Malaysia. By using a qualitative approach, a number of interview sessions have been conducted with the responsible departments at six established public universities in Malaysia. It has been found that currently only e-waste generated from used electric and electronic equipment (EEE) which have been declared as university assets are being managed by the responsible departments at the six universities. All six universities are using the standard process of handling their e-waste since every single process needs to comply with the requirements of the Ministry of Finance (MOF). This remains the main constraint to flexibility or improvement of the current system. The universities are also facing some challenges, among others, lack of awareness and knowledge on how to properly handle e-waste among university citizens, no clear sustainable policy at the university, lack of facilities within the university compound to support the proper practice of e-waste disposal as well as lack of education and related training provided to the campus citizens. This paper also provides some recommendations for the improvement of e-waste management towards sustainability and offers an agenda for future research.

263 Public perspectives of calf handling: A survey of Canadians

Abstract In North American beef production handling and restraint of young calves is integral to animal welfare and management practices. This study used a mixed method approach to gather public perceptions of three handling and restraint methods common on beef operations in western Canada (RW - Roping and Wrestling, NF – roping and NordForks, and TT – Tilt Table). Canadian participants (n = 551) recruited by CloudResearch to represent the national demographics participated in an online survey that included videos of each handling method to ascertain preferences and acceptability. Prior to watching the videos, participants were given industry information about handling and restraint or generic information regarding hay as a control information statement within the topic of agriculture. The survey also collected information about knowledge of the beef industry, animal welfare, and empathy toward animals. The reasons for preferences were described as presence of a perceived positive attribute and absence of a perceived negative for most preferred methods, and inversely when explaining least preferred method. The findings focused on calf’s experience, perception of handler actions, and pragmatic balancing of handler needs and a good life for the calf. Methods were rated as more acceptable for participants that ate meat consistently, knew more about the beef industry, and, to a lesser extent, if the individual had a lower animal empathy score (Table 1). Acceptability was not affected by providing information about the practices; however, information about handling and restraint did elicit more pragmatic reasoning. Most participants preferred TT over NF and RW (P &amp;lt; 0.001) and found TT more acceptable as well (P &amp;lt; 0.001). The TT was the most preferred method due to calf experience and human handling, notably the absence of dragging a calf, which was predominant in why participants selected NF or RW as their least preferred method. Consistency of themes highlights that the Canadian public has a fundamental expectation of value for the quality of life of the calf, humane handling, and pragmatism which aligns with beef sustainability initiatives and represent points of connection for building trust between industry and the public.

Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples.

In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.

Releaf: An Efficient Method for Real-Time Occlusion Handling by Game Theory.

Receiving uninterrupted videos from a scene with multiple cameras is a challenging task. One of the issues that significantly affects this task is called occlusion. In this paper, we propose an algorithm for occlusion handling in multi-camera systems. The proposed algorithm, which is called Real-time leader finder (Releaf), leverages mechanism design to assign leader and follower roles to each of the cameras in a multi-camera setup. We assign leader and follower roles to the cameras and lead the motion by the camera with the least occluded view using the Stackelberg equilibrium. The proposed approach is evaluated on our previously open-sourced tendon-driven 3D-printed robotic eye that tracks the face of a human subject. Experimental results demonstrate the superiority of the proposed algorithm over the Q-leaning and Deep Q Networks (DQN) baselines, achieving an improvement of 20% and 18% for horizontal errors and an enhancement of 81% for vertical errors, as measured by the root mean squared error metric. Furthermore, Releaf has the superiority of real-time performance, which removes the need for training and makes it a promising approach for occlusion handling in multi-camera systems.

Electrochemical Investigation of Moisture Byproducts in Molten Calcium Chloride

Residual water in molten CaCl2 reacts to form different byproducts, such as HCl, which can impact the corrosivity of the salt and efficiency of electrochemical operations, such as electrolytic oxide reduction and electrorefining. The ability to detect and quantify these byproducts electrochemically can provide feedback on the efficacy of vacuum drying and other purification methods, as well as the impact of these byproducts on process operations. An electrochemical signal’s association with the production of H2 is verified and characterized using cyclic voltammetry (CV) and residual gas analysis. CV estimated a 2-electron exchange process associated with H2 production. CV detected trace quantities of an oxidized species containing hydrogen in the salt on the order of 10 ppm. Different salt handling methods were compared for their impact on the hydrogen electrochemical signal. It was found that 30 min of exposure of CaCl2 in a beaker to low-humidity air (<20%) had minimal impact on the H2 production signal.

An improved permeability estimation model using integrated approach of hybrid machine learning technique and shapley additive explanation

Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production. Conventional methods relying on empirical correlations and assumptions often result in high costs, time consumption, inaccuracies, and uncertainties. This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. The GMDH-DE achieved an R2 of 0.9985, RMSE of 3.157, MAE of 2.366, and ME of 0.001 during training, and for testing, the ME, MAE, RMSE, and R2 were 1.3508, 12.503, 21.3898, and 0.9534, respectively. Additionally, the GMDH-DE demonstrated a 41% reduction in processing time compared to GMDH and RF. The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. Therefore, the GMDH-DE model offers a novel, efficient, and accurate approach for fast permeability prediction, enhancing hydrocarbon exploration and production.

Synthesis of expert matrices in inductive system-analytical research based on fuzzy logic algorithm

The object of research is the process of inductive modeling of complex systems. The studies that were conducted related to the application of algorithms of the fuzzy logic theory were to coordinate the conclusions of top-level experts in system information-analytical research (SIAR) in the tasks of innovative design. The possibilities of constructing elements of expert matrices of results, as well as evaluating the effectiveness of such applications, are defined. Thanks to this, obtaining formal expert evaluations in numerical form became possible. Experimental studies have confirmed that the proposed approach to the application of fuzzy logic algorithms to the construction of matrices of expert evaluations of SIAR results is quite effective and simple to implement. In addition, this approach fits well into the general paradigm of the Group Method of Data Handling (GMDH). In particular, it was established that the possibility of «retraining» such a block without significant efforts of professional experts can have a positive result, as well as have a good effect on the economic and time parameters of the research project. The main calculation formulas for the algorithm for building a fuzzy system using a neural network in a system with two rules are given. The construction of a fuzzy information output system trained on expert evaluations in the Matlab system is shown. As a result, a technologically acceptable standard deviation of 0.28268 mg/l was obtained. It has been established that by accumulating a database (knowledge) and/or using an information monitoring system, it is possible to «additionally train» a fuzzy system periodically or according to the established quality criterion in the program mode, without involving experts in this process. Thus, there are reasons to assert the importance of using a fuzzy system as one of the tools in inductive SIAR procedures

Modeling interfacial tension of methane-brine systems at high pressure high temperature conditions

In the present study, two white-box correlative models, namely gene expression programming (GEP) and group method of data handling (GMDH), were implemented to predict the interfacial tension (IFT) of brine-methane system. To achieve this, 790 data points were collected from literature, with pressure, temperature, and salinity as inputs and IFT as the output. The range of salinity, pressure, and temperature is from 0 to 107000 ppm, 0.01–260 MPa, and 278.1–477.59 K, respectively. The results show that the GMDH model with an R2 of 0.9321 and an average absolute percent relative error (AAPRE) of 3.68% outperforms the GEP, which has an AAPRE of 5.08% and an R2 of 0.8943. Based on our investigation, GMDH and GEP models follow the expected trend of IFT with the variation of pressure. It has been found that the GMDH model exhibits better performance compared to the previously developed correlations in the literature. Furthermore, the sensitivity analysis indicated that temperature has the greatest effect on IFT. By using the leverage approach, we proved that the GMDH model is statistically valid.