Processes, Volume 11, Issue 12 (December 2023) – 197 articles

The use of bacterial and microalgal consortia to remove nitrogen from wastewater has garnered attention as a potential alternative to conventional systems. This approach not only reduces energy consumption but also aids in nutrient recovery. Light is essential for algae photosynthesis; however, nitrifying bacteria are also influenced by light radiation. This mini-review summarizes the current knowledge concerning photoinhibition, the light stimulation of ammonia-oxidizing bacteria (AOB), resistance to light radiation, the implementation of microalgal–bacterial systems, and the possible mechanisms involved. Nitrosomonadaceae AOB and Nitrospiraceae nitrite-oxidizing bacteria (NOB) often coexist in a microalgal–bacterial system. Studies have suggested that AOB can tolerate light radiation at 200 μmol m⁻²·s⁻¹ in microalgal–bacterial systems, whereas NOB are almost completely suppressed, which can result in partial nitrification in the bioreactor. An appropriate light level can stimulate AOB growth in microalgal–bacterial granular reactors and may improve algae metabolic activity. Granular sludges or artificial “light-shielding hydrogel” could effectively protect nitrifying bacteria from light intensities up to 1600 μmol m⁻²·s⁻¹ in wastewater treatment reactors. Microalgal–bacterial systems along with the associated “algal shading effect” have been widely used in pond aquaculture. This approach minimizes the need for costly mechanical aeration through photo-oxygenation and facilitates nutrient recovery by filter-feeding fish. Full article

When a submersible is sitting on a seabed, it could lose buoyancy because of the bottom-sitting adsorption effect. In this article, a numerical calculation model and experimental scheme for eliminating the bottom-sitting adsorption effect of under-sea equipment were established. An analysis of the hydrostatic pressure variation on a submersible’s bottom was carried out, and a submerged water jet which was based on the method of soil liquefaction was proposed to solve the problem of reducing hydrostatic pressure. It was shown that a water jet could liquefy soil to restore hydrostatic pressure on the submersible’s bottom, and there was an optimal jet velocity to form the largest liquefied soil thickness. A rectangular pulsed jet was the best way to liquefy soil in terms of efficiency and the liquefaction degree, which can be seen from the calculation of the two-dimensional two-phase flow. Through the calculation of the three-dimensional two-phase flow, it was found that the soil liquefaction developed from the periphery to the center, and a variation in jet liquefaction with the top wall constraint was obtained. Finally, an experiment was carried out to prove that a submerged water jet could eliminate the bottom-sitting adsorption effect of a submersible. The results showed that the submerged jet was an efficient way to liquefy soil, and a submersible could quickly recover hydrostatic pressure on the bottom and refloat up independently. Full article

Thermochemical processes utilizing biomass demonstrate promising prospects for the generation of syngas. In this work, a gasification process employing combination of an indigenous low-grade coal with two distinct biomass sources, namely rice husk (RH) and wood sawdust (WS), was explored. The gasification of the selected feedstock was performed using a double-staged multi-opposite burner (MOB) gasifier. A 3D computational fluid dynamics (CFD) model was employed to analyze the effect of kinetic and diffusion rates on the overall gasification performance of an entrained flow biomass gasifier. DPM was employed to track the particles’ trajectory, while the gas phase was treated as the continuous phase, and its behavior was predicted using a standard k-epsilon turbulent model. To calculate both the homogeneous and heterogeneous reaction rates, the finite rate/eddy dissipation model was implemented. The findings indicate that the char conversion efficiency exceeded 95% across all instances. Among the different reaction schemes, scheme E (which involved complete volatile and char combustion reactions) produced better results in comparison with published results, with less than 1% error. Hence, scheme E was validated and utilized for the rest of the simulated cases. The feeding rate has an inverse effect on the overall performance of the gasifier. An increase in feed rate decreases the CO and H₂ composition in syngas. The maximum CO value was observed to be 57.59% at a 1.0 O/C ratio with a 0.005 kg/s feed rate, and the maximum H₂ value was observed to be 16.58% in the same conditions for Lakhra coal samples. In summary, Lakhra coal exhibited better performance than other biomass samples due to its better fixed carbon and volatiles in its composition. Full article

The power battery is an important component of new energy vehicles, and thermal safety is the key issue in its development. During charging and discharging, how to enhance the rapid and uniform heat dissipation of power batteries has become a hotspot. This paper briefly introduces the heat generation mechanism and models, and emphatically summarizes the main principle, research focuses, and development trends of cooling technologies in the thermal management of power batteries in new energy vehicles in the past few years. Currently, the commonly used models for battery heat generation are the electrochemical-thermal model and the electrical-thermal model. Scholars have conducted more research based on multidimensional electrochemical-thermal/electrical-thermal models because taking the actual characteristics of the battery into account can provide a more comprehensive and systematic description. Among various cooling technologies, the air-cooling system boasts the most economical manufacturing costs and a compact, reliable structure. The heat transfer coefficient of the liquid-cooling system is very high, while the temperature remains uniform in the PCMs cooling system during the material phase transition process. Against the background of increasing energy density in future batteries, immersion liquid phase change cooling technology has great development prospects, but it needs to overcome limitations such as high cost and heavy weight. Therefore, the current lithium-ion battery thermal management technology that combines multiple cooling systems is the main development direction. Suitable cooling methods can be selected and combined based on the advantages and disadvantages of different cooling technologies to meet the thermal management needs of different users. Full article

The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind and solar power poses challenges for the stable bundled dispatch of new energy. Leveraging the regulation flexibility of energy storage offers a potential solution to mitigate new energy fluctuations, enhance the flexibility of the hybrid energy systems, and promote bundled dispatch of new energy for external transmission. This paper takes energy storage as an example and proposes a capacity configuration optimization method for a hybrid energy system. The system is composed of wind power, solar power, and energy storage, denoted by the wind–solar–energy storage hybrid energy systems. The objective is to quantify the support provided by energy storage to bundled dispatch of new energy, namely determining the new energy transmission capacity that can be sustained per unit of energy storage. The results demonstrate that the proposed method effectively improves the bundled dispatch capacity of new energy. Moreover, the obtained configuration results can be tailored based on different wind–solar ratios, allowable fluctuation rates, and transmission channel capacities, rendering the approach highly valuable for engineering practicality. Full article

Due to the large non-homogeneity of coal reservoirs, there is a large uncertainty about the extent of the impact on coal bed methane production capacity. The Hanchengbei Block has the problems of early exploration, less available production data, and large variations in developed production capacity within a single well group during test production. Therefore, how to use the existing data to analyze the geological factors affecting the development of coalbed methane in the Hanchengbei Block is particularly important. In this paper, based on the coal seam properties and production characteristics of the Hanchengbei Block, a three-dimensional geological model of the area was meticulously constructed using Petrel 2015 modeling software. Through the utilization of stochastic modeling techniques, reservoir attributes were visualized in three dimensions, and probability distribution functions as well as confidence intervals for different geological parameters were derived through geological statistics. Building upon this foundation, a dual-layer geological model incorporating multiple factors was established using Comet3.0 numerical simulation software. Monte Carlo simulation methods were then employed to simulate the effects of various geological parameters on gas production, yielding corresponding simulation results. Through normalization processes, parameter sensitivity was analyzed to determine the primary controlling factors influencing production capacity. The results show that the thickness of the No. 5 coal seam in the Hanchengbei Block is mainly distributed in the range of 1.35–6.89 m; the gas content is 10.28–15.52 m³/t; and the permeability is 0.014–0.048 mD. Under their joint influence, the average gas production of Hanchengbei Block is between 310–720 m³/d. The main factors affecting the capacity of Hanchengbei Block are the thickness and gas content of the coal seam. This study can provide a basis for the subsequent optimization of favorable areas, the formulation of drainage systems, and the design and optimization of development well networks. Full article

The present work aims to answer three essential research questions (RQs) that have previously not been explicitly dealt with in the field of applied machine learning (ML) in steel process engineering. RQ1: How many training data points are needed to create a model with near-upper-bound predictive performance on test data? RQ2: What is the near-upper-bound predictive performance on test data? RQ3: For how long can a model be used before its predictive performance starts to decrease? A methodology to answer these RQs is proposed. The methodology uses a developed sampling algorithm that samples numerous unique training and test datasets. Each sample was used to create one ML model. The predictive performance of the resulting ML models was analyzed using common statistical tools. The proposed methodology was applied to four disparate datasets from the steel industry in order to externally validate the experimental results. It was shown that the proposed methodology can be used to answer each of the three RQs. Furthermore, a few findings that contradict established ML knowledge were also found during the application of the proposed methodology. Full article

To enhance the deflagration efficiency and protection level of combustible mixed gases in narrow spaces, a small square experimental pipeline system was designed. Experiments were conducted to investigate the effects of lateral vent pipes and metal wire meshes on the explosion characteristics of methane gas. This study examined the influence of changing the positions of the lateral vent pipes and metal wire meshes in the pipeline on the variation of parameters such as the flame shape, leading speed, and pressure of the methane/air premixed gas in the pipeline. The results indicated that the lateral vent pipes could effectively release part of the energy from the methane explosion, and the release effect was stronger the closer they were to the ignition end. This was significantly more effective in releasing the flame and pressure than when the vent pipes were placed in the middle or at the end of the pipeline. For lateral vent pipes close to the ignition source, their effective release of the not yet fully developed premixed flame allowed the heat absorption, wave absorption, and quenching performance of the installed metal wire mesh in the pipeline to fully exert their effects on the slow-spreading premixed flame. Furthermore, when a metal mesh was installed in the pipeline and the flame could not be extinguished, the flame penetrated the mesh structure, causing the flame front to become unstable and exhibit “irregular wrinkles”. That is, the flame front was no longer smooth, the wrinkles became more pronounced, and the degree of turbulence was enhanced. Full article

The emergence of bioplastics presents a promising solution to the environmental impact of the plastics industry. Bioplastics are engineered to degrade in aquatic or soil environments. However, not all bioplastics are completely biodegradable, and some, like petrochemical-based plastics, may contribute to plastic pollution. The biodegradability of bioplastics is significantly different in different environmental conditions such as soil, marine, and composting environments. At the same time, bioplastics produced from natural resources contain a mixture of known and unknown materials and show 32% cytotoxicity, 42% oxidative stress, 67% baseline toxicity, and 23% antiandrogenicity in bioassays. The extensive biodegradation of bioplastics in soil can also change the soil nutrients, leading to eutrophication or stunted plant growth. However, many concerns have arisen, according to which bioplastics may not be an alternative option for global plastic pollution in the long run, and limited studies focus on this scenario. This review aims to provide a comprehensive overview of the biodegradation of bioplastics in different environmental conditions and by microorganisms and their ecotoxicological impacts on soil and marine health. In conclusion, while bioplastics have the potential to be a sustainable alternative to conventional plastics, it is essential to address concerns regarding their complete biodegradability and toxicity. Therefore, sustainable methods must be used for their production and biodegradation to ensure a positive impact on the environment. Full article

This review explores eco-friendly methods for extracting bioactive natural products from diverse sources. The introductory exploration emphasizes the increasing demand for sustainable extraction methods, with a focus on the environmental impact of conventional approaches. Addressing existing knowledge gaps, this review outlines the key objectives of evaluating various green extraction technologies, including supercritical fluid extraction, pressurized liquid extraction, ultrasound-assisted extraction, enzyme-assisted extraction, and others. The primary findings underscore the remarkable potential and advancements achieved with green solvents, specifically deep eutectic solvents and bio-based solvents. This review elucidates the synergistic effects achieved by combining different extraction techniques, exemplified by ultrasound-microwave-assisted extraction and sequential supercritical fluid and pressurized liquid extraction, among others. Notwithstanding the promising results, this review emphasizes the importance of acknowledging and addressing challenges such as standardization, selectivity, scalability, and economic viability. Full article

Chromatographic separation plays a pivotal role in the manufacturing of chemical products and biopharmaceuticals. This technique exploits differences in distribution between stationary and mobile phases to separate mixtures, impacting final product quality. Simulated moving bed (SMB) technology, recognized for its continuous feed, enhances efficiency and increases production capacity while reducing solvent and water consumption. Despite its complexity in controlling variables like flow rates and valve switching times, traditional control theories fall short. This study introduces an intelligent fuzzy controller resembling an approximate neural network (NN) for SMB control. Simulation results demonstrate the controller’s effectiveness in achieving desirable outcomes for the SMB system. Full article

Retrofitting a tangentially fired boiler into a slag-tap boiler offers a solution for fully burning high-alkali coal in power plant boilers. Numerical simulation and hydrodynamic calculation of such a retrofit scheme were performed in this study. The maximum temperature in the furnace after retrofitting is 2306.8 K, surpassing the pre-retrofit temperature of 2095.8 K. The average temperature in the combustion chamber of the slag-tap boiler is 2080.3 K, which ensures that the slag can be discharged in a molten state. When the coal consumption is halved relative to the working condition of the boiler maximum continuous rating (BMCR) in the slag-tap boiler, the maximum temperature in the combustion chamber decreases from 2306.8 to 2220.3 K. However, the temperature distribution remains relatively uniform, ensuring that the slag discharge is not disrupted. In both of the working conditions calculated in this study, the fluid flow rates in the water-cold wall are positively correlated with the wall heat fluxes. The maximum wall temperatures under the two working conditions are 653.9 and 590.6 K, respectively, both of which are well within the safe limits for the wall material. The results illustrate the feasibility of the retrofit scheme. Full article

In order to extend grain’s storage time and ensure its quality, it is necessary to sort and clean it. The aim of this study was to justify the rational shape of the sorting chamber and the optimal technological parameters for the sorting of wheat grains in airflow. This study used newly designed grain sorting chambers with constant, widening, and narrowing cross-sections for the airflow sorting of “Skagen” wheat grain. The aerodynamic properties of wheat grains were investigated when moisture was at 14 ± 2.0%. The grain flow rate in the chambers varied from 4 to 12 kg min⁻¹ every 2 kg min⁻¹. In addition, the airflow velocity varied from 8 to 12 m s⁻¹ every 1 m s⁻¹. The tilt angle of the constant cross-section camera was increased to 5°. Experimental studies have determined a terminal airflow velocity of 11.53 m s⁻¹ for wheat grains. At the terminal airflow velocity, the grain flight coefficient was obtained to be about 0.074. These studies showed that the narrowing chamber is preferable for lower grain flow rates compared to the constant cross-section of the chamber. The widening chamber requires a lower airflow velocity to achieve the same performance and quality as the other chambers. Full article

Maize straw has been widely used for the production of energy through anaerobic digestion, but biogas production can be hindered by a lack of trace elemental nutrients. To address this issue, a lab-scale anaerobic plug flow reactor was continuously operated at 55 °C for 300 days, with a hydraulic retention time of 42 days and an organic loading rate of 2.1 g total solids/(L·day). Results from this study showed that between days 101 and 194, the methane yield slightly decreased from 0.26 ± 0.04 to 0.24 ± 0.03 L/g volatile solids (VS), but significant volatile fatty acid accumulation was observed by reaching up to 2759 ± 261 mg/L. After trace elements were added to the reactor, the methane yield increased to 0.30 ± 0.03 L/g VS, with 53% methane content. Around 62% of the total chemical oxygen demand and volatile solids were broken down into methane. Volatile fatty acid levels dropped and stabilized at around 210 ± 50 mg/L, indicating restored process stability. The addition of trace elements increased the abundance of Firmicutes and decreased Synergistetes in bacteria while simultaneously increasing the abundance of Methanosarcina in archaea. In conclusion, trace element supplementation was experimentally found to be necessary for stable thermophilic anaerobic digestion of maize straw. Full article

Raman spectroscopy is a useful tool for polymorphic form-monitoring during the crystallization process. However, its application to solute concentration estimation in two-phase systems like crystallization is rare, as the Raman signal is influenced by various changing factors in the crystallization process. The development of a robust calibration model that covers all variations is complex and represents a major challenge for the implementation of Raman spectroscopy for in-line monitoring and control of the solution crystallization process. This paper describes the development of a Raman-based calibration model for estimating the solute concentration of the active pharmaceutical ingredient ceritinib. Several different calibration approaches were tested, which included both temperature and spectra of clear solutions and slurries/suspensions. It was found that the concentration of the ceritinib solution could not be accurately predicted when suspended crystals were present. To overcome this challenge, the approach was enhanced by including additional variables related to crystal size and solid concentration obtained via in-line process microscopy (chord-length distribution percentiles D10, D50 and D90) and turbidity. Partial least squares regression (PLSR) and artificial neural network (ANN) models were developed and compared based on root mean square error (RMSE). ANN models estimated the solute concentration with high accuracy, with the prediction error not exceeding 1% of the nominal solute concentration. Full article

A hybrid desulfurization process combining a physical filtration stage on cellular concrete (CC abiotic filter, called CCAF) and a biotrickling filter (called BTF) filled with expanded schist as packing material was used to remove high H₂S concentrations from a synthetic gas containing dinitrogen (N₂), dioxygen (O₂) and H₂S without the addition of a nutritive solution. Provided that small amounts of oxygen are present in the gas (1.2 ± 0.1% in volume), the global removal efficiency was 100%, and the global removal capacity reached 35 ± 2 gH₂S m⁻³ h⁻¹ for a total empty bed residence time (EBRT) of 120 s (CCAF + BTF). The resilience of the desulfurization process was demonstrated by applying severe changes in the H₂S concentrations, from 160 to 1150 ± 20 mg m⁻³ for an EBRT = 120 s. According to the performances of the abiotic filter, which can decline over time due to the lifetime of the cellular concrete (137 days), the biotrickling filter reacted either as a refining system or as an efficient system able to treat significant H₂S loading rates (up to 45 ± 3 gH₂S m⁻³ h⁻¹). Depending on the operating conditions, the increase in the pressure drops of the biotrickling filter (from 45 ± 3 to 234 ± 8 Pa m⁻¹) highlighted biomass accumulation, especially extremophilic Acidithiobacillus sp. Considering the cellular concrete abiotic filter alone, removal capacities of up to 56 ± 3 gH₂S m⁻³ h⁻¹ were recorded for an EBRT of 60 s, demonstrating that gases such as landfill biogas or household biogas could be efficiently treated using this simple technique. Full article

Accidental gas release is a major triggering event for the offshore oil and gas industry. This paper focuses on the experimental and numerical investigation for dispersion behavior of released gas on offshore platforms. For this purpose, an experimental system is designed and developed to investigate gas release and dispersion. A series of experiments are carried out, among which the scenarios with constant leakage rates and time-varying leakage rates are both emphasized. The gas concentrations at different sampling points are obtained to study the dispersion behavior and accumulation characteristics of the released gas. Furthermore, a numerical computational fluid dynamics model is established to replicate the experimental scenarios. Good agreement between experimental data and CFD simulation results is observed by calculating a series of statistical performance measures. The developed numerical model is subsequently utilized to investigate a gas release scenario on a practical offshore platform, in which a fully transient leakage rate is adopted considering the response of process protection measures. The developed numerical model could provide support for risk assessment and optimization of contingency plans against gas release accidents in offshore facilities. Full article

Various types of pores, including organic and inorganic variations, exhibit distinct impacts on the storage capacity of shale gas reservoirs and play a significant role in shale gas occurrence. However, there is a limited number of studies that have quantitatively addressed the developmental characteristics of these diverse pore types and their primary controlling factors. This paper explores the development of inorganic pores, specifically interparticle pores and intraparticle pores, as well as organic matter (OM) pores within the shales of the Wufeng–Longmaxi Formation in the Upper Yangtze region. Parameters such as areal porosity, pore diameter, and pore number based on the FE-SEM and image digitization are discussed. Additionally, the influence of the sedimentary environment on the development of various pore types through integrated wavelet transform techniques and geochemical analysis are analyzed. This analysis reveals the distinctive mechanisms governing the development of pore types under the sequence stratigraphic constraints. The findings reveal that the Wufeng–Longmaxi Formation within the study area can be classified into four systems tracts (transgressive systems tracts TST1 and TST2, and highstand systems tracts HST1 and HST2). Within TST1+HST1, OM pores emerge as the predominant pore type, contributing to over 65% of the porosity. TST2 similarly displays OM pores as the dominant type, comprising over 45% of the total porosity, with an average OM areal porosity of 7.3%, notably lower than that of TST1+HST1 (12.7%). Differences in OM pore development between TST1+HST1 and TST2 shales are attributed to variations in OM abundance and type. In HST2, inorganic pores are the dominant pore type, primarily consisting of interparticle pores associated with clay minerals, contributing to more than 50% of the porosity, while OM pores remain almost undeveloped. The frequent sea level fluctuations during the sequence stratigraphic evolution caused variations in sedimentary environments across different depositional sequences. These differing depositional environments lead to varying OM content and types, mineral genesis, and content, ultimately resulting in disparities in the development of shale pore types within different sequences. Full article

The biosynthesis of citric acid (CA) and its derivatives is of great interest due to its wide range of applications in various manufacturing sectors. The fungus Aspergillus niger is mainly used for the commercial production of CA, using sucrose and molasses as the primary carbon sources. Since the 1960s, intensive research has been underway to introduce Yarrowia lipolytica yeast as an alternative to traditional fungal technology. This review discusses the practical uses of CA and its derivatives. Also, the challenges and developments that have led to efficient and green CA synthesis technologies using Y. lipolytica are outlined. The nutrient medium requirements and the use of various carbon sources, encompassing pure substrates and industry, agriculture, and food waste are considered. Additionally, the choice and improvement of strain producers, including efficient mutagenesis, genetic modification, and screening methods, are discussed. Full article

The dynamic job-shop scheduling problem is a complex and uncertain task that involves optimizing production planning and resource allocation in a dynamic production environment. Traditional methods are limited in effectively handling dynamic events and quickly generating scheduling solutions; in order to solve this problem, this paper proposes a solution by transforming the dynamic job-shop scheduling problem into a Markov decision process and leveraging deep reinforcement learning techniques. The proposed framework introduces several innovative components, which make full use of human domain knowledge and machine computing power, to realize the goal of man–machine collaborative decision-making. Firstly, we utilize disjunctive graphs as the state representation, capturing the complex relationships between various elements of the scheduling problem. Secondly, we select a set of dispatching rules through data envelopment analysis to form the action space, allowing for flexible and efficient scheduling decisions. Thirdly, the transformer model is employed as the feature extraction module, enabling effective capturing of state relationships and improving the representation power. Moreover, the framework incorporates the dueling double deep Q-network with prioritized experience replay, mapping each state to the most appropriate dispatching rule. Additionally, a dynamic target strategy with an elite mechanism is proposed. Through extensive experiments conducted on multiple examples, our proposed framework consistently outperformed traditional dispatching rules, genetic algorithms, and other reinforcement learning methods, achieving improvements of 15.98%, 17.98%, and 13.84%, respectively. These results validate the effectiveness and superiority of our approach in addressing dynamic job-shop scheduling problems. Full article

Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods. Full article

It is challenging for the European power system to exactly predict RES output and match energy production with demand due to changes in wind and sun intensity and the unavoidable disruptions caused by severe weather conditions. Therefore, in order to address the so-called “flexibility challenge” and implement the variable RES production, the European Union needs flexible solutions. In order to accommodate quicker reactions, compared to those performed today, and the adaptive exploitation of flexibility, grid operators must adjust their operational business model, as the electrical grid transitions from a fully centralized to a largely decentralized system. OneNet aspires to complete this crucial step by setting up a new generation of grid services that can fully utilize distributed generation, storage, and demand responses while also guaranteeing fair, open, and transparent conditions for the consumer. Using AI methods and a cloud-computing approach, the current work anticipates that active management of the power system for TSO–DSO coordination will be improved by the web-based client-server application F-channel. In the current work, a user’s experience with the platform for a Business Use Case (BUC) under the scenario of severe weather conditions is presented. The current work aims to increase the reliability of outage and maintenance plans for the system operators (SOs) by granting them a more accurate insight into the conditions under which the system may be forced to operate in the upcoming period and the challenges that it might face based on those conditions. In this way, the methodology applied in this case could, via AI-driven data exchange and analyses, help SOs change the maintenance and outage plans so the potential grave consequences for the system can be avoided. The SOs will have accurate forecasts of the relevant weather parameters at their disposal that will be used in order to achieve the set targets. The main results of the presented work are that it has a major contribution to the optimal allocation of the available resources, ensures the voltage and frequency stability of the system, and provides an early warning for hazardous power system regimes. Full article

Although analytical solutions for the problem of diffusion-controlled drug release from uniform formulations of simple geometries, like slabs, spheres, or cylinders, are well known, corresponding exact expressions for the average release times are not widely used. However, such exact analytical formulae are very simple and useful. When the drug is initially distributed homogeneously within the matrix, the average time of release from a sphere of radius R is $t_{av}=(1/15)R^2/D$ and from a slab of thickness L is $t_{av}=(1/12)L^2/D$, where D is the corresponding drug diffusion coefficient. Regarding cylindrical tablets of height H and radius R, simple analytical expressions are obtained in the two opposite limits of either very long ($H\gg R$) or very short ($H\ll R$) cylinders. In the former case, of practically radial release, the average release time is $t_{av}=(1/8)R^2/D$, while in the latter case the same result as that of a slab with thickness H is recovered, $t_{av}=(1/12)H^2/D$, as expected. These simple and exact relations are useful not only for an estimate of the average release time from a drug carrier device when diffusion is the dominant mechanism of drug delivery, but also for the experimental determination of the drug diffusion coefficient in a release system of interest through the measured release profile, given the mean squared size of the formulation. Full article

Metabolic pathway prediction and reconstruction play crucial roles in solving fundamental and applied biomedical problems. In the case of fundamental research, annotation of metabolic pathways allows one to study human health in normal, stressed, and diseased conditions. In applied research, it allows one to identify novel drugs and drug targets and to design mimetics (biomolecules with tailored properties), as well as contributes to the development of such disciplines as toxicology and nutrigenomics. It is important to understand the role of a metabolite as a substrate (the product or intermediate participant of an enzymatic reaction) in cellular signaling and phenotype implementation according to the pivotal paradigm of biology: “one gene–one protein–one function (one trait)”. Due to the development of omics technologies, a vast body of data on the metabolome composition of living organisms has been accumulated over the past two decades. Systematization of the information on the roles played by metabolites in implementation of cellular signaling, as well as metabolic pathway reconstruction and refinement, have necessitated the development of bioinformatic tools for performing large-scale omics data mining. This paper reviews web-accessible databases relevant to metabolic pathways and considers the applications of the three types of bioinformatics methods for constructing metabolic networks (graphs for substrate–enzyme–product transformation; stoichiometric analysis of substrate–product transformation; and product retrosynthesis). It describes, step by step, a generalized algorithm for constructing biological pathway maps which explains to the researcher the workflow implemented in available bioinformatics tools and can be used to create new tools in projects requiring pathway reconstruction. Full article

Quantum dots (QDs) are emerging as promising candidates for innovative memristive materials, owing to their distinct surface, quantum size, and edge effects. Recent research has focused on tailoring QDs with specific organic molecules to fine-tune charge transfer states between the host and grafted species, as well as enhancing their dispersibility and processability. Violet phosphorus (VP), a newly discovered two-dimensional phosphorus allotrope, offers excellent carrier dynamics, predictable modifiability, and superior oxidation resistance, making it a promising contender in this domain. In this study, we synthesized a rich azobenzene-containing star-shaped polymer diazonium salt (AzoSPD) to functionalize violet phosphorus quantum dots (VPQDs), with the dual objectives of enhancing organic dispersibility and introducing photo-switching capabilities. The synthesized AzoSPD–VPQDs exhibit intramolecular charge transfer characteristics under electrical stimuli of ambient conditions, displaying significant non-volatile rewriteable memory properties and a substantial switching ratio exceeding 2 × 10³. Furthermore, the high resistance state (HRS) current can be enhanced by nearly 40 times under 465 nm illumination, enabling optoelectronic information sensing and storage within a single device. This work not only provides insights into enhancing the optoelectronic properties of QDs through functional organic molecular modification but also represents a pioneering exploration of the potential applications of VPQDs in novel memristors. Full article

Mycotoxins are toxic compounds produced as secondary metabolites by certain types of filamentous fungi under specific conditions. The contamination of nuts and nut-related products with mycotoxins is a significant global concern due to their severe consequences on human health, including carcinogenicity and immunosuppression. Aflatoxins, with a particular emphasis on aflatoxin B1, are the most common and toxic mycotoxins found in human food. Aflatoxin B1 (AFB1) is known to be highly toxic and carcinogenic. Consequently, global food regulatory organizations have established permissible levels for mycotoxins in nuts. Numerous methodologies have been developed for the detection of mycotoxins in nuts. However, high-performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QqQ-MS/MS) have shown clear benefits in terms of effectiveness and sensitivity. This review aims to provide a comprehensive overview of the major mycotoxins found in nuts, their physiological effects, and their worldwide prevalence. Additionally, the review will focus on nut sample pretreatment methods, analytical techniques employed for mycotoxin detection in nuts, and recent advancements in materials and solvents used for this purpose. Significant gaps exist in mycotoxin detection in nuts, including methodological variability and insufficient data from certain nut-producing countries that need further exploration in the future. Full article

Municipal solid waste contains a high percentage of organic waste, and when it is not disposed of, it becomes a threat to the environment by contaminating the air, water, and soil. Composting is one of the recovery techniques in which the end product of waste eventually contributes to the agriculture industry, reducing the harmful effects on the environment. Composting municipal solid waste is a clean and effective technique for waste disposal. The mechanized composting process is carried out by several methods, like the windrow method or the rotary drum method. However, large-scale composting processes involve energy consumption and labor costs for waste preparation and handling. This increases the market cost of compost. Hence, an energy-efficient composting technique with minimum environmental impact is needed. This research work aims to analyze the performance of an energy-efficient spouted bed technique for aerobic composting of municipal solid waste for the first time using spouted bed technology with sand as the bed material. Spouted bed composting handles the waste using a pneumatic method with minimum power consumption in comparison to conventional mechanical methods with windrow processes or rotary composting machines. The experimental procedure involves a test run of waste along with bed material and the collection of temperature variations, pH variations, moisture variations, and volatile matter content during the progression of the composting process. The results of this experimental study on a single batch of waste are then used to analyze the quality of the compost generated and compare it with existing results. Specific energy consumption for the process was less than 800 kJ/ton of raw waste input, which is much less than the energy used for conventional composting techniques. pH, volatile content, moisture, and temperature measurements indicated agreement with the established parameters of the composting process. Full article

During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system. Full article

This study successfully achieved high-precision detection of the clean coal ash content in the coal froth flotation domain by integrating deep learning with the likelihood function. Methodologically, a novel data processing and prediction framework was established by combining a deep learning Keras neural network with the likelihood function from probability statistics. The SIFT algorithm was utilized to extract key feature points and descriptors from the images, and keypoint matching and mean-shift clustering algorithms were employed to accurately obtain information on foam motion trajectories and velocities. For parameter optimization, the maximum likelihood estimation was applied to find the optimal parameter estimates of the likelihood function, ensuring enhanced model accuracy. By incorporating the optimized likelihood function parameters into the Keras deep neural network, an efficient prediction model was constructed for the dosage of flotation reagents, froth velocity, and clean coal ash content. The model’s evaluation involved six performance metrics. The experimental results were highly significant, with R² at 0.99997%, RMSE at 0.04458%, MAE at 0.00170%, MAPE at 0.02329%, RRSE at 0.00994%, and MAAPE at 0.00067%. Full article

To study the influence of different drainage methods on the production performance of coal measure gas wells, the interbedded reservoir composed of coal and shale in the Longtan Formation of the Dahebian block was used as the research object. Considering the influence of coal and shale matrix shrinkage, effective stress, and interlayer fluid flow on reservoir properties such as fluid migration behavior and permeability, a fluid–solid coupling mathematical model of coal measure superimposed gas reservoirs was established. Numerical simulations of coal measure gas production under different drainage and production modes were conducted to analyze the evolution of reservoir pressure, gas content in the matrix, permeability, and other characteristic parameters of the superimposed reservoir, as well as differences in interlayer flow. The results showed that, compared to single-layer drainage, cumulative gas production increased by 33% under multi-layer drainage. Both drainage methods involve interlayer energy and substance transfer. Due to the influence of permeability, porosity, and mechanical properties, significant differences exist in reservoir pressure distribution, preferential flow direction, gas content in the matrix, and permeability ratio between coal and shale reservoirs under different drainage and production modes. Multi-layer drainage effectively alleviates the influence of vertical reservoir pressure differences between reservoir layers, facilitates reservoir pressure transmission in shale reservoirs, enhances methane desorption in shale matrices, promotes matrix shrinkage, and induces the rebound of shale reservoir permeability, thus improving overall gas production. Full article