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Due to the abundant use of granular materials in chemical industries, it is inevitable to store raw materials and products in bulk in silos. For this reason, much research has been carried out in the field of construction, operation and maintenance of silos. One of the important issues that must be investigated in silos is the behavior of their structure when the materials inside them are unloaded. Structural vibrations and the creation of normal noise usually discharge the granular of material from the silo. Both of phenomena are undesirable due to the problems they can cause to the structure and its surroundings. According to the said issues, this paper aims to investigate the vibration problem of the sulfur storage silo of the first refinery during discharge with the help of measuring experimental vibration data and simulating the silo model.

In the experimental investigation, the main cause of the vibration of the 400-ton silo in the refinery is used. The mass asymmetry phenomenon when the silo is filled is also considered. The experimental results are authenticated by software analysis too.

The results showed that the natural frequency of the ninth mode is almost equal to the natural frequency of sulfur discharge from the silos and has the largest shape change in the structure and vibration range. It is also concluded that the larger sulfur silo (400 tons) should be prioritized over the smaller sulfur silo (200 tons) in the emptying program, and the 400 tons silo should never be emptied even through the 200 tons silo is empty.

An attempt is made to investigate the issue of vibration in sulfur storage silos in the first refinery of South Pars in the form of experimental investigation and modal analysis.

This study aims to assess the pitting resistance of austenitic stainless steel welded joints fusion zone (FZ) with high density of inclusions before and after surface treatment, including potentiostatic pulse technique (PPT) and pickling.

The potentiodynamic polarization tests and critical pitting temperature tests were carried out for estimating pitting resistance. The PPT and pickling were performed as surface treatment. Scanning electron microscope (SEM) and energy dispersive spectrometer were used for characterize the microstructure and elemental distribution. Electron back-scattered diffraction (EBSD) was used to assess the portion of phases and morphology of grains.

The weld metal exhibits a higher degree of alloying compared to the base metal, and it contains d-phase and sulfur-containing inclusions. Sulfur-containing inclusions serve as initiation sites for pitting, and they diminish the pitting resistance of weld metal. Both PPT and pickling can remove sulfur-containing inclusions, but PPT causes localized dissolution of the weld metal matrix around the inclusions, while pickling does not. Because of the high density of inclusions, certain pits initiated by PPT are significantly deeper, which makes the formation of stable pitting easier. Because of the high density of inclusions, certain pits initiated by the PPT are deeper. This characteristic facilitates the progression of these initial defects into fully developed, stable pits.

Analysis of pitting initiation in shielded metal arc welding FZ with PPT and ex situ SEM tracking observation. Explanation of why the PPT surface treatment is not able to enhance the pitting resistance of stainless steel with a high inclusion density.

The purpose of this study is to create sustainable additives for future vehicles, characterized by low levels of sulfated ash, sulfur and phosphorus (SAPS) or even SAPS-free alternatives. These newly developed additives must not only match or outperform the current commercial benchmarks in terms of tribological performance, but also align with the emerging sustainability trends. It is anticipated that this innovative technology will yield promising outcomes in the realm of hybrid and electric vehicles.

This research primarily focused on chemical synthesis, performance evaluation and characterizations. These aspects were studied through collaboration between Syensqo, Southwest Research Institute (the USA) and the Lab of the Future in France. The data was generated and analyzed by a team of research scientists, internship students and technical specialists.

Two types of additives have been specifically designed and synthesized in accordance with sustainable requirements. Both technologies have exhibited exceptional frictional and wear-resistant properties. Moreover, the leading candidates exhibit a lower rate of copper corrosion, stable electric conductivity and outstanding thermal stability when compared to commercial benchmarks. This study is expected to open a new research avenue for developing next-generation additives for lubricants, with wide potential applications including hybrid electric vehicle and electric vehicle markets.

In the current lubricant market, there is a lack of effective low-SAPS or SAPS-free additives. This research aims to address this gap by designing sustainable additives for next-generation vehicles that not only meet specific requirements but also maintain optimal lubrication performance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0033/

The purpose of this paper was to study the color change kinetics of lac dye in response to aldehydes, carbon dioxide and other food spoilage metabolites for its potential application in intelligent food packaging.

UV–Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A₅₂₈/A₄₈₈) with time using zero and first-order reaction kinetics. Lac dye-based indicator was prepared to validate the result of study for monitoring quality of strawberries.

Lac dye was orange-red in acidic medium and purple in alkaline medium. Color change of dye in response to benzaldehyde followed zero-order reaction kinetics, whereas for carbon dioxide first-order model was found best. No color change of dye solution was observed for alcohols, ketones and sulfur compounds. In the validation part, the color of the indicator label changed from purple to orange when the strawberries spoiled.

The study expands application area for lac dye as sensing reagent in intelligent food packaging for spoilage or ripeness detection of fruits and vegetables.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

In the denim industry, enzyme washing and its combination with stone washing are generally used to get the desired worn-out look. However, these conventional methods include high water, energy and time consumption. Nowadays, laser fading, which is a computer-controlled, dry, ecological finishing method, is preferred in the denim fading process. The purpose of this study is to observe the effects of chemical pretreatment applications on laser-faded denim fabric in terms of color and mechanical properties. To eliminate the enzyme washing process in denim fading and to minimize the disadvantages of laser fading, such as decreased mechanical properties and increased fabric yellowness, various chemical pretreatment applications were applied to the denim fabric before laser fading, followed by simple rinsing instead of enzyme washing.

Two different indigo-dyed, organic cotton denim fabrics with different unit weights were exposed to pretreatment processes and then laser treatment, followed by simple rinsing. Polysilicic acid, boric acid, borax and bicarbonate were used for pretreatment processes, and laser treatment was carried out under optimized laser parameters (40 dpi resolution and 300 µs pixel time). Tensile strength was tested, and color values (CIE L*, a*, b*, ΔE*, C* and h), color yield (K/S), yellowness and whiteness indexes were measured to identify the color differences.

Before laser fading, 30 g/L and 40 g/L polysilicic acid pretreatments for sulfur-indigo-dyed fabric and a mixture of 10 g/L boric acid and 10 g/L borax pretreatments for the fabric only indigo-dyed were recommended for the laser fading with sufficient mechanical properties and good color values.

With the chemical pretreatments defined in this study, it was possible to reduce yellowness and maintain the mechanical properties after laser fading, thus minimizing the disadvantages of laser treatment and also eliminating enzyme washing.

The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the entire life cycle. And this research provides a comprehensive assessment model that incorporates the release of gaseous pollutants and particulate matter during the whole life cycle of C&DW, thereby contributing to a more holistic understanding of its impact on human health.

The research was conducted in two stages. Firstly, the quantitative model framework of pollutants emitted by C&DW was established. Three types of pollutants were considered, namely nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and inhalable particulate matter (PM10). Second, disability-adjusted life year (DALY) and willingness to pay (WTP) assessments were used to provide a monetary quantified health impact for pollutants released by C&DW.

The results show that the WTP value of PM10 is the highest among all pollutants and 8.68E+07 dollars/a, while the WTP value in the disposal stage accounts for the largest proportion compared to the generation and transportation stage. These findings emphasize the importance of PM10 and C&DW treatment stage for pollutant treatment.

The results of this study are of great significance for the management department to optimize the construction management scheme to reduce the total amount of pollutants produced by C&DW and its harm to human health. Meanwhile, this study fills the gap in existing research on the impact assessment of C&DW on human health throughout the whole life cycle, and provides reference and basis for future research and policy formulation.

At present, research on the preparation of corrosion inhibitors using modified pyrimidine derivatives is still blank. The purpose of this study is to synthesize a new cationic mercaptopyrimidine derivative quaternary ammonium salt, known as DTEBTAC, that can be used as a corrosion inhibitor to slow down the metal corrosion problems encountered in oil and gas extraction processes.

A new corrosion inhibitor was synthesized by the reaction of anti-Markovnikov addition and nucleophilic substitution. The weight loss method was used to study the corrosion inhibition characteristics of synthetic corrosion inhibitors. Electrochemical and surface topography analyses were used to determine the type of inhibitor and the adsorption state formed on the surface of N80 steel. Molecular dynamics simulations and quantum chemistry calculations were used to investigate the synthetic corrosion inhibitor’s molecular structure and corrosion inhibition mechanisms.

The results of the weight loss method show that when the dosage of DTEBTAC is 1%, the corrosion rate of N80 steel in hydrochloric acid solution at 90? is 3.3325 g m-2 h-1. Electrochemical and surface morphology analysis show that DTEBTAC can form a protective layer on the surface of N80 steel, and is a hybrid corrosion inhibitor that can inhibit the main anode. Quantum chemical parameter calculation shows that DTEBTAC has a better corrosion inhibition effect than DTP. The molecular dynamics simulation results show that DTEBTAC has stronger binding energy than DTP, and forms a network packing structure through hydrogen bonding, and the adsorption stability is enhanced.

A novel cationic mercaptopyrimidine derivative quaternium-ammonium salt corrosion inhibitor was designed and provided. Compared with the prior art, the preparation method of the synthesized mercaptopyrimidine derivative quaternary ammonium salt corrosion inhibitor is simple, and the presence of nitrogen-positive ions, sulfur atoms and nitrogen-rich atoms has an obvious corrosion inhibition effect, which can be used to inhibit metal corrosion during oil and gas field exploitation. It not only expands the application field of new materials but also provides a new idea for the research and development of new corrosion inhibitors.

This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.

The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.

CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂ indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.”

The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.

Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.