Improve Process Efficiency Research Articles

Detects Damage Car Body using YOLO Deep Learning Algorithm

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remote sensing data-driven landslide prediction methods have attracted increasing attention. However, the lack of an effective and efficient paradigm for organizing multi-source remote sensing data and a unified prediction workflow often results in the weak generalization ability of existing prediction models. In this paper, we propose an improved multi-source data-driven landslide prediction method based on a spatio-temporal knowledge graph and machine learning models. By combining a spatio-temporal knowledge graph and machine learning models, we establish a framework that can effectively organize multi-source remote sensing data and generate unified prediction workflows. Our approach considers the environmental similarity between different areas, enabling the selection of the most adaptive machine learning model for predicting landslides in areas with scarce samples. Experimental results show that our method outperforms machine learning methods, achieving an increase in F1 score by 29% and an improvement in processing efficiency by 93%. Furthermore, by comparing the susceptibility maps generated in real scenarios, we found that our workflow can alleviate the problem of poor prediction performance caused by limited data availability in county-level predictions. This method provides new insights into the development of data-driven landslide evaluation methods, particularly in addressing the challenges posed by limited data availability.

Research on Abrasive Water Jet Polishing of Silicon Carbide Based on Fluid Self-Excited Oscillation Pulse Characteristics

A self-excited oscillating pulsed abrasive water jet polishing method is proposed to solve the problems of low removal efficiency in traditional abrasive water jet polishing and the influence of an external flow field on the material surface removal rate. The self-excited oscillating chamber of the nozzle was used to generate pulsed water jets to reduce the impact of the jet stagnation zone on material surface removal and increase the jet speed to improve processing efficiency. ANSYS Fluent was employed to simulate the processing flow field characteristics for different lengths of oscillation cavities. The simulation results indicate that the velocity of the jet shaft reached a maximum of 178.26 m/s when the length of the oscillation cavity was 4 mm. The erosion rate of the material is linear with the processing angle. A nozzle with a length of 4 mm of the self-excited oscillating cavity was fabricated for SiC surface polishing experiments. The results were compared with those of ordinary abrasive water jet polishing. The experimental results showed that the self-excited oscillation pulse fluid enhanced the erosion ability of the abrasive water jet on the SiC surface and significantly improved the material-removal depth of the abrasive water jet polishing SiC. The maximum surface erosion depth can be increased by 26 μm.

Applications of Industry 4.0 Technologies in Warehouse Management: A Systematic Literature Review

Background: Recent literature indicates that warehouse management costs account for a significant portion of overall logistics costs in companies. Warehousing requires the classification, controlling and management of inventory as well as processing of related information. Therefore, adopting efficient and reasonable warehouse management measures to achieve effective management and control of materials is a key means to flexibly adjusting the supply and demand of storage materials and reduce operating costs. There remains a gap in the understanding of benefits and barriers to the full adoption of Industry 4.0 technologies and decision support systems (DSSs) in warehouse management. Methods: This work applies a systematic literature review methodology of recent implementation case studies to analyze documented barriers and benefits of Industry 4.0 technology adoption in warehouse management. For analysis, benefits and barriers are ranked in order of importance using Pareto analysis based on their frequency of occurrence. Results: Improved process efficiency, the availability of real-time data, added competitive advantage and the ability to integrate business activities digitally are the top four most important benefits of implementing Industry 4.0 technologies and decision support systems in warehouse management. The prominent barriers to implementation are high life cycle cost, challenging physical environment/layout, inadequate supporting resource constraints, increased security risk and high energy consumption. Conclusions: Barriers to implementing Industry 4.0 technologies are interrelated in nature and prevent businesses from realizing the full benefit of implemented Industry 4.0 technologies. Adequate financial support, new knowledge and skills are required to be able to ensure the successful implementation of Industry 4.0 in warehousing management.

Integration of dedusting and denitration in V2O5-WO3/TiO2/PTFE-based catalytic bag-filter materials: Lab- and pilot-scale testing and development of SCR models

Removal of dust and NOx from flue gas is highly significant for air pollution control, and integration of these two functions in one unit can greatly improve process efficiency. In this study, a functional catalytic bag-filter material (CBFM) was fabricated from commercial PTFE felt and cheap catalyst V2O5-WO3/TiO2 via a simple immersion-fixation procedure. The attachment of catalysts on PTFE was confirmed, and the material properties of CBFM of various loadings were determined. Their denitration and dedusting performances were investigated individually in lab-scale experiments. Compared to the original PTFE, CBFMs exhibited superior denitration activities, which were positively correlated with temperature (60–90% under 150–210 °C) and negatively correlated with filtration velocity (50–90% under 0.5–0.9 m min−1). On the other hand, introduction of catalysts in PTFE felt did not compromise its filtration efficiency, and almost complete dust removal was achieved. A simplified kinetic model was developed based on Eley-Rideal mechanism, to correlate the materials' denitration efficiency with various operational conditions. The model's accuracy was verified in both lab and pilot environments, where real flue gas from a waste incineration plant was treated with a scaled-up CBFM. In a six-day continuous test, the CBFM maintained >70% NO removal and >99.8% dust removal, confirming its stability and applicability in industrial settings. Therefore, this study offers a low-cost and functional bi-functional filter material capable of efficient simultaneous dedusting/denitration, as well as a simple and practical procedure for its manufacture. In addition, the kinetic model could be applied to speed up production and process optimization, and direct future application of similar materials in real environments.

Ultrasound assisted extraction: A relook at solvent to material ratio, its effects on process efficiency and how it can be exploited for different uses

AbstractUltrasound assisted extraction (UAE) is one of the preferred green extraction techniques widely used for the extraction of bioactive compounds from plant materials. It has attracted much attention and many studies have been done to understand its mechanism, effects and efficiency. However, no study has discovered and given enough attention to the superior role solvent‐to‐material ratio (SMR) plays in moderating UAE efficiency. This study therefore emphasizes the importance of SMR as the main determinant of UAE efficiency and further proves its mechanistic effects with ultrasound physics. It discusses SMR effects on extraction medium density, acoustic energy transfer, cavitation rate, power intensity attenuation and free hydroxyl radical generation during water‐based UAE. This understanding is needed to assist researchers in the design of their extraction experiments and optimizations. Without this insight, even optimization processes will be limited in giving the true conditions needed for the highest UAE efficiency. The study shows that low SMR increases acoustic energy scattering and absorption and attenuates ultrasound power intensity and vice versa. SMR can be controlled to either preserve bioactivity of compounds or produce free radicals for microbial inactivation and elimination and oxidative degradation of natural pigments in aqueous media.Practical applicationA comprehensive understanding of the mechanism and physics behind the effects of solvent‐to‐material ratio (SMR) during ultrasound assisted extraction (UAE) of bioactive compounds helps in fine‐tuning SMR purposely to ensure that ultrasound power attenuation is minimized. Ultrasound power defines UAE efficiency by virtue of acoustic cavitation. Due to the fact that UAE in aqueous media generates free hydroxyl radicals (*OH) from water autolysis, SMR can be deliberately tuned to induce controlled power attenuation to limit this phenomenon. This becomes a strategy to protect bioactive compounds that are prone to oxidative degradation. As ultrasound power is linked to energy consumption, fine‐tuning SMR to minimize power attenuation can significantly improve process efficiency and reduce treatment time, saving energy and enhancing profitability, especially during large scale industrial treatments.

Lévy Flights Diffusion with Drift in Heterogeneous Membranes

The modelling of diffusion in membranes is essential to understanding transport processes through membranes, especially when it comes to improving process efficiency. The purpose of this study is to understand the relationship between membrane structures, external forces, and the characteristic features of diffusive transport. We investigate Cauchy flight diffusion with drift in heterogeneous membrane-like structures. The study focuses on numerical simulation of particle movement across different membrane structures with differently spaced obstacles. Four studied structures are similar to real polymeric membranes filled with inorganic powder, while the next three structures are designed to show which distribution of obstacles can cause changes in transport. The movement of particles driven by Cauchy flights is compared to a Gaussian random walk both with and without additional drift action. We show that effective diffusion in membranes with an external drift depends on the type of the internal mechanism that causes the movement of particles as well as on the properties of the environment. In general, when movement steps are provided by the long-tailed Cauchy distribution and the drift is sufficiently strong, superdiffusion is observed. On the other hand, strong drift can effectively stop Gaussian diffusion.

Enhanced compatibility and functionality of thermoplastic cassava starch blended PBAT blown films with erythorbate and nitrite

Improved miscibility between thermoplastic starch (TPS) and polybutylene adipate-co-terephthalate (PBAT) enhances processability and properties of TPS-based biodegradable plastic packaging. This research investigated compatibility and functionality of TPS/PBAT (50/50) blends with sodium nitrite and sodium erythorbate (1–5%) via blown film extrusion. Film morphology and mechanical and barrier properties were investigated. Sodium nitrite and sodium erythorbate improved processing efficiency of TPS, modified film flexibility and enhanced physical and chemical compatibility between TPS and PBAT matrices via hydrolysis, confirmed by 1H NMR and ATR-FTIR analyses. These chemical reactions also affected thermal and phase transition behaviors. Increased starch granule dispersion caused smoother microstructure, resulting in higher oxygen barrier. Sodium nitrite and sodium erythorbate functionalized TPS/PBAT films reduced discoloration of packaged cured meat during storage at 4 °C for 9 days. These compounds provided extra functionality and improved compatibility between TPS and PBAT biodegradable plastic blends for novel and sustainable food packaging.

Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

Most satellite-derived bathymetry (SDB) methods developed thus far from passive remote sensing data have required in situ water depth, thus limiting their utility in areas with no in situ data. Recently, new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) observations have shown great potential in providing high-precision a priori water depth benefits from range-resolved lidar. In this study, we propose a combined active and passive remote sensing SDB method using only satellite data. An adaptive ellipse DBSCAN (AE-DBSCAN) algorithm is introduced to derive a priori bathymetric data from ICESat-2 data to automatically adapt to the terrain change complexity, and then we use these a priori bathymetric data in Sentinel-2 images to help build a model between remote sensing reflectance (Rrs) and water depth. Three machine learning (ML) methods are then employed, and the performances compared with conventional empirical SDB models are presented. After that, the results using different Sentinel-2 Rrs band combinations and the effects with and without atmospheric correction on ML-based SDB are discussed. The results showed that our AE-DBSCAN method performs better than the standard DBSCAN method, and the ML-based SDB can achieve an overall RMSE of less than 1.5 m in St. Thomas better than the traditional SDB method. They also indicate that ML-based SDB can obtain a relatively high-precision water depth without atmospheric correction, which helps to improve processing efficiency by avoiding a complex atmospheric correction process.

A Post-Evaluation System for Smart Grids Based on Microservice Framework and Big Data Analysis

Wind energy, as a clean energy source, has been experiencing rapid development in recent years. However, there is often a significant difference between the designed electricity generation capacity and the actual electricity generation capacity during the construction of wind farms, making it difficult to assess the economic benefits of wind farms. Therefore, the development post-evaluation technology is required to support the renovation of old wind farms. In addition, traditional data analysis techniques are unable to handle and analyze massive data in a timely manner. Therefore, big data technology must be developed to improve processing efficiency. To address these issues and meet actual business needs, this paper designs an intelligent grid electricity generation post-evaluation platform for wind farms based on a microservice framework and big data analysis technology. The platform evaluates the operating status of wind farms by analyzing their operational and design data and visualizes relevant big data information. It provides technical support and improvement solutions for wind farm renovation and new wind farm construction. The platform has been tested and proven to meet the requirements for processing and analyzing massive data, post-evaluating electricity generation, and visualization.

Analysis of Standard Operational Procedure Submission of Special Ceiling For Sales Division

This study aims to analyze the standard operating procedure (SOP) for filing an exceptional ceiling for the sales division at PT. Semen Baturaja (Persero) Tbk. This study uses a qualitative research design with a case study approach. Data was collected through interviews, document analysis, and observation of the SOP submission process for the sales division at PT. Semen Baturaja (Persero) Tbk. This study's findings are whether the Standard Operational Procedure for Submission of Special Ceilings has been going well or not. Standard operating procedures that are running well are shown in the Special SOP for Ceiling Submission; this can happen because it is in the preparation process so that, in practice, SOPs can be easily understood and also efficiently implemented by distributors. While implementing the main things in the Standard Operating Procedures is good because it has been appropriately implemented in every job, there's just no workflow in the Sales Division. In addition, there are due distributor receivables, so it becomes an obstacle for distributors to apply for a Special Ceiling according to the Standard Operating Procedure (SOP). This research contributes to the literature on SOPs by highlighting the importance of well-defined and effectively implemented SOPs in the context of the sales division of PT. Semen Baturaja (Persero) Tbk. In addition, this study provides insight into the challenges and factors that may influence SOP implementation in this environment. Practical implications for the management of PT. Semen Baturaja (Persero) Tbk and sales division. The findings indicate the need for more detailed and simplified SOPs for special ceiling proposals to improve process efficiency. In addition, this study recommends that companies provide adequate training and support for employees involved in the process. This research is limited to one case study at PT. Semen Baturaja (Persero) Tbk and these findings may not be generalizable to other companies or industries. In addition, this study does not evaluate the impact of implementing improved SOPs on sales division performance. Future research can explore these areas and assess the effectiveness of various strategies to improve the unique ceiling application process in sales divisions in peer companies in Indonesia.

Controller Design and StabilityAnalysis of Intensification Process using Analytical Exact Gain-Phase Margin approach.

The controllable intensified process has received immense attention from researchers in order to deliver the benefit of process intensification to be operated in a desired way to provide a more sustainable process toward reduction of environmental impact and improvement of intrinsic safety and process efficiency. Despite numerous studies on gain and phase margin approach on conventional process systems, it is yet to be tested on intensified systems as evidenced by the lack of available literature, to improve the controller performance and robustness. Thus, this paper proposed the exact gain and phase margin (EGPM) through analytical method to develop suitable controller design for intensified system using Proportional-Integral-Derivative (PID) controller formulation, and it was compared to conventional Direct Synthesis methods (DS), Internal Model Control (IMC), and Industrial IMC method in terms of the performance and stability analysis. Simulation results showed that EGPM method provides good setpoint tracking and disturbance rejection as compared to DS, IMC, and Industrial IMC while retaining overall performance stability as time delay increases. The Bode Stability Criterion was used to determine the stability of the open-loop transfer function of each method and the result demonstrated decrease in stability as time delay increases for controllers designed using DS, IMC, and Industrial IMC, and hence control performance degrades. However, the proposed EGPM controller maintains the overall robustness and control performance throughout the increase of time delay and outperform other controller design methods at higher time delay with [Formula: see text] uncertainty test. Additionally, the proposed EGPM controller design method provides overall superior control performance with lower overshoot and shorter rise time compared to other controllers when process time constant is smaller in magnitude ([Formula: see text]) than the instrumentation element, which is one of the major concerns during the design of intensified controllers, resulting overall system with a higher order. The desired selection of gain margin and phase margin were suggested at 2.5 to 4 and 60 °-70[Formula: see text],respectively, for a wide range of control conditions for intensified processes where higher instrumentation dynamic would be possible to achieve robust control as well. The proposed EGPM method controller is thought to be a more reliable design strategy for maintaining the overall robustness and performance of higher order and complex systems that are highly affected by time delay and high dynamic response of intensified processes.

On the role of multifunctional ionic liquids for the oxidative carboxylation of olefins with carbon dioxide

Ionic liquids (ILs) are catalysts with profound effects on the activity and selectivity for the transformation of CO2. In particular, highly functionalisable and immobilisable imidazolium based ILs (ImILs) activate CO2 molecules and have demonstrated activity towards CO2 cycloaddition reactions. The addition of hydroxyl groups has also been reported to enhance the cycloaddition reaction. An ideal system would combine both functionalities for superior performance. In addition, integrating the cycloaddition with the epoxidation of olefins employing multifunctional catalysts in one-pot would improve process efficiency, reduce waste and costs. However, the interaction between the cation-anion and the hydroxyl groups needs to be elucidated to realize these advantages. Herein, the viability of using ILs as bifunctional catalyst for one-pot reaction is evaluated by combining theoretical and experimental studies. Density functional theory (DFT) simulations of both reactions using halogenated and hydroxyl functionalized ImLs are presented. The position of the hydroxyl group close to the imidazolium unit primarily influences the opening of the epoxide and its activation for the cycloaddition reaction, thus facilitating the catalysis. In addition, experimentally the selectivity increases considerable with the catalyst immobilization. For the epoxidation reaction the DFT calculations and experimental results demonstrated favoring hydrolysis reaction.

Effect of Salt Content on Biogas Production and Microbial Activity: Review Study

Over the years, biogas production technology has advanced with the goal of reducing process costs and greenhouse gas emissions, and increasing biogas production. Several design factors and operational aspects must be taken into account in order to produce a stable and efficient biogas. When the substrates contain high salts, anaerobic treatment will be ineffective due to the disadvantages of high energy input and operating cost, membrane contamination, and low efficiency. This indicates that the treatment of high salinity organic waste is a big challenge. High salinity levels had a negative effect on bacterial growth through bacterial osmotic pressure metabolism. For example, high salinity can alter the course of fermentation and the accumulation of volatile fatty acids at high concentrations, as well as cause a decrease in methane yield and maximum rate of methane production, prolonging the late-stage period. A low level of salt concentration encourages the growth of bacteria since sodium is essential for the growth and metabolism of microorganisms in AD systems. When the sodium salt concentration is less than 8 g/L, there is no significant inhibition in the generation of methane. Addition of >8 g/L NaCl, however, significantly reduced methane production (causing 17-80 percent inhibition). This paper focuses on understanding in detail how NaCl affects methane production and microbial activity, report salt concentrations that improve process efficiency and reduce inhibition, as well as review the modified kinetic model and demonstrate the effect of salt on methane production and delay in methanogenesis.

Efficient and low-damage machining of Ti6Al4V: laser-assisted CBN belt grinding

ABSTRACT This study introduces a new method for efficient and low-damage machining of Ti6Al4V: laser-assisted CBN belt grinding. The impact of laser power and grinding depth on the machining behavior of the material is investigated, the elemental distribution and chemical state of the ground surface are analyzed, and the removal mechanism of laser-assisted CBN belt grinding is described. The results show that increasing laser power will improve processing efficiency, enhance plastic material removal, and improve surface quality; the tangential grinding force is decreased by 63.5% and the normal grinding force is decreased by 72.6% at the maximum laser power; a large amount of Ti and N are present on the ground surface, and they are present in the form of TiN compounds. This study is a guide to the research on efficient and low-damage machining of hard-to-process materials like titanium alloys.

Integrating BIM and Blockchain across construction lifecycle and supply chains

The Construction industry has a complex structure with multiple parties involved, which often leads to “adversarial relationships”, “risk avoidance”, and a “lack of trust” among the different actors. This culture is further compounded by a “linear workflow” that often results in low efficiency, delays, rework and unnecessary waste. Blockchain technology can help to mitigate these issues by creating a decentralised and transparent system, where all the actors can have access to a shared database, it allows tracking and monitors the different stages of the project, and even automate some processes increasing efficiency and reducing delays and rework.This study highlights the advantages of Blockchain technology, particularly how it can provide a single source of truth for project data while allowing multiple parties to access and share data in a secure and transparent way, improving the workflow of BIM projects and decreasing the likelihood of errors, mistakes, or fraudulent activities. The paper explores the integration of BIM and Blockchain across life cycle and supply chains based on the RIBA plan of work, with the objective to streamline collaboration while improving process efficiency and resource traceability in projects. The study proposes a roadmap performing a detailed literature survey for Blockchain adoption in the construction industry, and validated on a real-world Bridge project. Furthermore, this study is innovative since it examines the integration of BIM and Blockchain throughout the entire project lifecycle by simulating the smart contract implementation based on the RIBA plan of work, thus providing an in-depth examination of the potential benefits of this integration.

Prediction of tool wear in milling process based on BP neural network optimized by firefly algorithm

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development.

Reduced processing efficiency impacts auditory detection of amplitude modulation in children: Evidence from an experimental and modelling study

Detection of amplitude modulations (AM) improves until 10 years of age. This development may not be explained only by sensory maturation but also by improvements in processing efficiency: the ability to make efficient use of available sensory information. This hypothesis was tested on 86 6-to-9-year-olds and 15 adults using AM-detection tasks assessing sensitivity, masking and response consistency. Sensitivity was estimated by the detection thresholds of a sinusoidal AM applied to a pure-tone carrier; AM masking was estimated as threshold elevation produced when replacing the pure-tone carrier by a narrowband noise; response consistency was estimated using a double-pass paradigm where the same set of stimuli was presented twice. Results showed that AM sensitivity improved from childhood to adulthood. AM masking did not change with age, indicating that the selectivity of AM filters was adult-like by 6 years. However, response consistency increased developmentally, suggesting reduced processing efficiency in childhood. At the group level, double-pass data were well simulated by a model of the auditory system assuming higher internal noise for children than adults. At the individual level, a sub-optimal decision strategy was added to better capture inter-individual variability. Thus, both systematic and stochastic inefficiencies may explain worse AM detection in childhood.

Hurdle effect of thermosonication and non‐thermal processing on the quality characteristics of fruit juices: An overview

AbstractTraditional thermal processing technologies are widely used to extend the shelf life of fruit juices. However, its detrimental effect on the nutritional and sensory properties of fruit juices paved the way for alternative food processing technologies. Non‐thermal processing methods can be a substitute for thermal methods because they have no/minimal effect on the nutritive and sensory qualities of fruit juices and improve their storage quality by deactivating enzymes and eliminating microorganisms. In this review article, combined use of thermosonication (TS) with other non‐thermal processing technologies in fruit juice processing has been detailed. Additionally, the impact of TS on the various qualities of fruit juices as well as the TS mechanism have been covered. TS, one of the emerging non‐thermal technologies have been preferred in fruit juice and beverage processing industries over the past few years, due to consumer's preference towards high quality and nutritious food products. When TS is employed in combination with other non‐thermal processing methods, it reduces processing time, improves process efficiency, and reduces power consumption. The content of this review paper might be beneficial for upcoming research and the commercialization of this hurdle‐based approach.Practical ApplicationsGenerally, fruit juices were treated using thermal methods (e.g., pasteurization) for extending the shelf life by inactivating microbial and enzymatic activities. But it shows negative impact on the nutritional and sensorial attributes of fruit juices. Non‐thermal technologies like, TS has been preferred as an alternative method for the processing of juices due to their less/no effect on the quality parameters of juices. The benefits of TS as compared with thermal treatments include less flavor loss, significant energy savings, and increased homogeneity. TS reduces the processing temperature and time required for pasteurization of juices and it can be used to make high‐quality fruit juices that are additive‐ and preservative‐free. In the review, the effect of TS on fruit juice quality as well as its combined effect with high pressure processing and pulse electric field processing has been summarized. The finding of this review paper might be helpful for the further exploration of these technologies in the large‐scale food industries.

Bioreactors and biophoton-driven biohydrogen production strategies

Given the current issues with global warming and rising greenhouse gas emissions, biohydrogen is a viable alternative fuel option. Technologies to produce biohydrogen include photo fermentation, dark fermentation, direct and indirect bio-photolysis, and two-stage fermentation. Biological hydrogen generation is a green and promising technique with mild reaction conditions and low energy consumption compared to thermochemical and electrochemical hydrogen generation. To optimize hydrogen gas output using this method, the activity of hydrogen-consuming bacteria should be restricted during the production stages of hydrogen and acetate to prevent or limit hydrogen consumption. Raw material costs, poor hydrogen evolution rates, and large-scale output are the main limitations in biological hydrogen generation systems. Organic wastes would be the most preferred target feedstock for hydrogen fermentation, aside from biodegradable wastes, due to their high amount and simultaneous waste treatment advantage. This study examined the three primary methods for converting waste into bio-hydrogen: microbial electrolysis cell, thermochemical gasification, and biological fermentation, from both a technological and environmental standpoint. The effectiveness and applicability of these bioprocesses in terms of aspects influencing processes and their constraints are discussed. Alternative options for improving process efficiency, like microbial electrolysis, bio-augmentation, and multiple process integration, are also considered for industrial-level applications. Biohydrogen generation might be further enhanced by optimization of operating conditions and adding vital nutrients and nanoparticles. Cost reduction and durability enhancement are the most significant hindrances to fuel-cell commercialization. This review summarizes the biohydrogen production pathways, the impact of used organic waste sources, and bacteria. The work also addresses the essential factors, benefits, and challenges.