Efficiency Of Process Research Articles

Classification of real and bogus transients using active learning and semi-supervised learning

The mounting data stream of large time-domain surveys renders the visual inspections of a huge set of transient candidates impractical. Techniques based on deep learning-based are popular solutions for minimizing human intervention in the time domain community. The classification of real and bogus transients is a fundamental component in real-time data processing systems and is critical to enabling rapid follow-up observations. Most existing methods (supervised learning) require sufficiently large training samples with corresponding labels, which involve costly human labeling and are challenging in the early stages of a time-domain survey. One method that can make use of training samples with access to only a limited amount of labels is highly desirable for future large time-domain surveys. These include the forthcoming 2.5-meter Wide-Field Survey Telescope (WFST) six-year survey and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance. They have been applied to the task of the classification of real and bogus transients. Unlike most existing approaches, which necessitate massive and expensive annotated data, we aim to leverage training samples with only 1000 labels and discover real sources that vary in brightness over time in the early stages of the WFST six-year survey. We present a novel deep learning method that combines active learning and semi-supervised learning to construct a competitive real-bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results, compared to using only the limited labeled data. Our proposed methodology capitalizes on the potential of active learning and semi-supervised learning. To demonstrate the efficacy of our approach, we constructed three newly compiled datasets from the Zwicky Transient Facility (ZTF), achieving average accuracies of 98.8<!PCT!>, 98.8<!PCT!>, and 98.6<!PCT!> across these three datasets. It is important to note that our newly compiled datasets only work in terms of testing our deep learning methodology and there may be a potential bias between our datasets and the complete data stream. Therefore, the observed performance on these datasets cannot be assumed to directly translate to the general alert stream for general transient detection in actual scenarios. The algorithm will be integrated into the WFST pipeline, enabling an efficient and effective classification of transients in the early period of a time-domain survey.

Plantwide Control for the Separation of THF-H2O in an Azeotropic Distillation Process

This paper presents a plantwide control strategy for optimizing a pressure-swing azeotropic distillation process used in tetrahydrofuran dehydration. Leveraging Skogestad’s methodology, this strategy focused on two distillation columns: a low-pressure column for water recovery at 20 psia and a high-pressure column that achieved 0.99 molar fraction purity of tetrahydrofuran at 115 psia. This study identified critical control variables through plant analysis by implementing PI controllers in the regulatory control layer to stabilize flows and pressures. In the supervisory control layer, a PI controller combined with MIMO MPC effectively enhanced the product purity and reduced the energy consumption by 36%. Stable inlet and outlet flow conditions (100 lbmol/hr inlet, 29.59 lbmol/hr outlet) were maintained without compromising the equipment integrity. The operational ranges for the process included variations in the tetrahydrofuran mole fraction from 0.25 to 0.35 at the inlet, which demonstrated a robust performance across perturbations. These achievements signify significant advancements in process efficiency and sustainability, offering substantial reductions in energy usage while ensuring consistent high purity levels in tetrahydrofuran production. The developed control structure sets a new standard for efficient azeotropic distillation processes, with implications for enhancing operational performance across industrial applications.

A review on pharmaceutical pollutants removal in water solution by catalytic ozonation using zeolite

AbstractThe presence of pharmaceutical pollutants in water sources constitute a serious risk to human health and the environment. Catalytic ozonation has emerged as a promising strategy for reducing these pollutants. This procedure uses ozone with the help of catalysts to improve the oxidation of organic molecules. Recently, there has been a lot of interest in using zeolite as an ozonation catalyst in the elimination of pharmaceutical contaminants from water solutions. Zeolites have unique properties such as their high surface area, porosity, and ion‐exchange capabilities, that make them effective catalysts for the decomposition of ozone and the oxidation of organic pollutants into harmless byproducts. This study aims to investigate the efficiency of zeolite catalytic ozonation in the elimination of pharmaceutical pollutants from aqueous solutions encompassing the working mechanisms, the determinants affecting the process's efficiency, potential obstacles, and perspective avenues for advancement within this field.

Research on Roof Cutting and Pressure Relief Technology of Gob-side Entry Retaining with Hard Roof

Traditional coal pillar roadway protection is easy to cause roadway stress concentration, roadway deformation instability, and low recovery rate of coal resources. In order to solve the stability of roadway mining process and coal mine efficiency. Aiming at the problem of gob-side entry protection by deep hole pre-splitting blasting with hard roof, taking 1207 working face of Jiegou Coal Mine as the engineering background, through theoretical analysis, numerical simulation, field industrial test and other methods, the technology of roof cutting and pressure relief and constant resistance anchor cable reinforcement and roadway protection is put forward, and the key parameters such as appropriate roof cutting height and roof cutting angle are analyzed to achieve the effect of roof cutting and pressure relief, and the roadway reinforcement support scheme is designed. The field research shows that the roof cutting and roadway protection technology has a good control effect on 1207 working face roadway, realizes the safe production of working face, and provides certain reference value for this similar condition.

INFLUENCE OF HELICAL CUTTING-EDGE ANGLE ON END-MILLING STABILITY

Increasing productivity, machining accuracy and efficient use of resources are important priorities for companies that manufacture competitive products. One of the main problems that hinders these processes is the vibration that occurs during cutting. Various methods are used to suppress vibration, one of which is the use of tools with a variable helical cutting-edge angle. However, when choosing the cutting-edge angle, it is important to consider the types of vibrations that occur during cutting, as they directly affect the efficiency of the milling process. In addition, the use of tools with different cutting-edge geometries, such as wavy, gives positive results in roughing, but becomes ineffective in finishing. The purpose of this paper is to study the effect of the helical cutting-edge angle on the stability of the end-milling at different cutting speeds. Both theoretical aspects and experimental data are considered, which make it possible to evaluate the effectiveness of using tools with different angles of inclination to ensure the stability of the machining process and increase productivity while minimizing vibrations in the most unfavorable third speed zone of oscillations for cutting. To conduct the experiments, a special stand was used to adjust the stiffness of the workpiece, record the vibrations that occur during cutting, and the time of contact between the workpiece and the tool. The milling was performed in the third high-speed oscillation zone using a tool whose design provides for the possibility of adjusting the angle of inclination of the helical cutting edge. Studies confirm that changing the angle of inclination can significantly affect the stability of the milling process, reducing the intensity of oscillations and improving machining accuracy. However, this effect depends on the initial cutting conditions, such as the cutting speed. With its increase, the amplitude of the accompanying free oscillations increases, regardless of the value of the angle of inclination. Ensuring the stability of the end-milling in the third speed zone by changing the angle of inclination is possible only at the speeds that determine the beginning of this zone. However, within the entire speed range covered by the third speed zone, it is impossible to ensure a stable milling process only due to the angle of inclination. The study emphasizes the importance of an integrated approach to selecting cutting parameters to achieve process stability.

Development of Tools for the Automatic Processing of Advanced Driver Assistance System Test Data

Advanced driver assistance system (ADAS) technologies are key to improving road safety and promoting innovation in the automotive sector. The approval and analysis of ADAS systems, especially automatic emergency braking (AEB) tests, require complex procedures and in-depth data management. This work presents innovative tools developed to facilitate the post-processing of ADAS AEB test data, created in collaboration with Nardò Technical Center. The tool, called Autonomous Code Generation Intelligence (ACGI), introduces an intuitive and intelligent user interface that helps analyze and interpret ADAS test approval regulations. ACGI automates the generation of code sections within a data analytics framework, streamlining the compliance process and significantly reducing the time and programming skills required. This tool allows engineers to focus on high-value tasks, improving overall process efficiency. To achieve this objective, the tool encodes the DAART code framework (Data Analysis and Automated Report Tool) which allows users to carry out real post-processing of the tests conducted on the track. The results demonstrate that both tools simplify and automate critical steps in the ADAS automatic emergency braking test data analysis process. In fact, the tool not only improves the accuracy and efficiency of the analyses but also offers a high degree of customization, making it a flexible and adaptable tool to meet the specific needs of users. In future developments, ACGI could be extended to cover additional ADAS tests and could be equipped with artificial intelligence to suggest configurations based on new regulations.

Conceptual Design of Simulation-Based Approach for Robotic Automation Systems: A Case Study of Tray Transporting

This study investigated the application of robotic automation in food manufacturing, focusing on enhancing tray transporting operations through a simulation-based approach. The findings primarily focused on bakery production but also demonstrate broader applicability to other sectors that involve repetitive and labor-intensive tasks. The researchers analyzed worker fatigue and limited productivity associated with manual tray handling. To evaluate these issues, simulations were conducted for two scenarios (Case A and Case B), applying robotic automation systems at different stages of production. Key performance indicators (throughput and utilization rates) were analyzed to assess improvements in process efficiency and reductions in worker strain. The results showed that robotic automation significantly increased throughput by 83.7% in simpler processes and by 27.1% in more complex ones, highlighting the impact of task complexity on automation effectiveness. Workforce demands decreased and demonstrated the potential of automation to alleviate physical strain in repetitive tasks. Simulations provided insights into workflow optimization, confirming their value as reliable tools for planning and refining automation strategies. The proposed framework offers a flexible and scalable solution for enhancing efficiency and consistency in manufacturing. Future research should apply similar approaches to other industries and explore the integration of human and robotic labor to further optimize safety, productivity, and cost effectiveness.

Stochastic Plantwide Optimizing Control for an Acrylic Acid Plant

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations.

Effective recycling of mixed spent lithium ion batteries using sodium persulfate, and saponified Cyanex 272

ABSTRACT The presence of critical metals like lithium, manganese and cobalt is crucial in recycling spent lithium-ion batteries (SLIB). This study focuses on recovering these metals from SLIB powder through leaching, precipitation and solvent extraction. Key parameters affecting process efficiency were evaluated, along with the mechanism and kinetics of metal recovery. Optimal leaching rates of 96.8% lithium, 99.1% manganese, and 97.9% cobalt were achieved using 27% (v/v) HCl at 70°C and 10% (w/v) pulp density over 2 hours. Manganese was selectively precipitated with sodium persulphate at pH 1.3 for 4 hours. Lithium and cobalt were separated from the Mn-free solution using Na-Cyanex 272 at equilibrium pH 5.5, yielding a cobalt separation factor of 80.5. The McCabe-Thiele plot indicated two extraction stages with an aqueous-to-organic ratio of 1:1, achieving 99.6% cobalt extraction and 8% lithium co-extraction. Cobalt was back-extracted with 6% (v/v) HCl. The proposed process flow sheet offers a simple, effective method for metal recovery, supporting recycling businesses in extracting valuable resources efficiently.

Polymerizable Ionic Liquid-Based Gel Polymer Electrolytes Enabled by High-Energy Electron Beam for High-Performance Lithium-Ion Batteries

Polymerizable ionic liquid-based gel polymer electrolytes (PIL-GPEs) were developed for the first time using high-energy electron beam irradiation for high-performance lithium-ion batteries (LIBs). By incorporating an imidazolium-based ionic liquid (PIL) into the polymer network, PIL-GPEs achieved high ionic conductivity (1.90 mS cm−1 at 25 °C), a lithium transference number of 0.62, and an electrochemical stability exceeding 5 V. E-beam irradiation enabled rapid polymer network formation within a metal-cased battery structure, eliminating the need for initiators and improving the process efficiency. In the NCM811/PIL-GPE/Li cells, PIL-GPE (8:2) delivered an initial discharge capacity of 198.8 mAh g−1 with 82% retention at 100 cycles, demonstrating enhanced thermal stability and cycling performance compared to traditional GPEs. The demonstrated PIL-GPEs demonstrate strong potential for high-stability, high-performance LIB applications.

A CAM-UNet for pose determination of supercapacitor cell module assembly based on monocular vision

Purpose This paper aims to solve the problems of low stacking efficiency and long production time in the supercapacitor module assembly process, a stacking system based on monocular vision is proposed, including bracket visual positioning, grasping and stacking, and it is applied in actual production. Design/methodology/approach To enhance the robustness of the workpiece location method and improve the location accuracy, the improved U-Net network and image processing algorithms are used to segment the collected images. In addition, for the extracted feature points, the objective function that can be globally optimized is obtained by parameterizing the rotation matrix to construct a polynomial equation system and, finally, the equation system is solved to obtain the final pose estimation, which could improve the accuracy of workpiece location. Findings The result indicates that the proposed method is successfully performed on the manipulator. Besides, this method can well solve the problem of object reflection on the conveyor belt. The Intersection over Union of the image segmentation of the object is 0.9948, and the Pixel Accuracy is 0.9973, which has a high segmentation accuracy for the image. The error range between the method proposed in this paper and the pose estimation is within 2 mm, and the qualified rate of supercapacitor module stacking products is over 99.8%. Originality/value This paper proposes a method of accurately extracting feature points by integrating an improved U-Net network and image processing and uses the workpiece positioning algorithm of the optimal solution PnP problem algorithm. The calculation results show that the algorithm improves the positioning accuracy of the workpiece, realizes the assembly of stacked supercapacitor modules and is applied in industrial production.

Exploring the Microbial Ecology of Nitrification Denitrification in Wastewater Treatment: Innovations and Future Directions

The nitrification-denitrification process serves as a critical mechanism in the biological removal of nitrogen from wastewater, a necessity for mitigating the environmental impact of nitrogen compounds on aquatic ecosystems. This complex process is orchestrated by diverse microbial communities, whose ecological interactions and functional capabilities are central to the efficiency and stability of nitrogen removal. In recent years, advances in molecular biology and microbial ecology have shed new light on the intricate dynamics within these microbial communities, revealing the roles of lesser known microbial taxa and the importance of microbial community structure and function in optimizing nitrification denitrification processes. This review provides a comprehensive examination of the microbial ecology involved in nitrification and denitrification, exploring the roles of key microbial players, including ammonia oxidizing bacteria (AOB), Nitrite oxidizing Bacteria (NOB), ammonia oxidizing archaea (AOA), and denitrifying bacteria. The review also delves into the environmental factors that influence microbial community dynamics, such as pH, temperature, oxygen availability, and the presence of inhibitory substances. By understanding these factors, the review highlights the challenges of maintaining stable and efficient nitrogen removal processes, particularly in the face of environmental fluctuations and operational disturbances, the review discusses recent innovations in wastewater treatment technologies that leverage microbial ecology to enhance process efficiency. These include bio-augmentation strategies, where specific microbial strains are introduced to bolster nitrification and denitrification, and advanced bioreactor designs, such as moving bed biofilm reactors (MBBRs) and membrane bioreactors (MBRs), which provide optimized environments for microbial growth and activity. The integration of anaerobic ammonium oxidation (anammox) processes, which offer significant energy savings and reduce greenhouse gas emissions, is also explored as a promising alternative to traditional nitrification pathways.

Nigeria at Crossroad: Re-positioning the Nation Politically and Economically

Nigeria, Africa’s largest economy, faces persistent political and economic challenges, and widespread poverty. This article examines the country’s woes and proposes comprehensive solutions. The paper argues that Nigeria’s transformation hinges on institutional reforms, economic diversification and inclusive governance. Recommendations such as enhancing the transparency and efficiency of the electoral process, effective decentralization strategy, strengthening anti-corruption institutions and economic diversification through agriculture and infrastructural development, implementing these measures requires a strong political will and leadership commitment, citizens engagement and participation, and strategic successful implementation of these measures as outlined in the paper can yield to improved governance and transparency, economic growth, diversification, and political participation, among others. This article concludes by providing a road map for Nigeria’s political and economic resurgence, highlighting the need for collective action and commitment from government, citizens and stakeholders.

Affinity purification of the outer hair cell motor protein prestin using His-tag

ObjectiveThe high sensitivity and broad frequency selectivity of mammalian hearing are associated with the somatic motility of outer hair cells (OHCs) in the cochlea. This motility is considered to be induced by conformational changes of the motor protein prestin expressing in the lateral plasma membrane of OHCs. Since its identification in 2000, prestin has been actively investigated and its structure and function have gradually been elucidated. These successes are partly due to the development of efficient expression and purification system of the membrane proteins including prestin. To obtain further understandings of prestin, the development of various types of such systems will be essential. However, recent study protocols on membrane proteins have often employed HEK293 cells and have become complexed with expression genes carrying several proteins and peptides for stabilization and purification of the expressed proteins. In the present study, a simple expression and purification system using Chinese hamster ovary (CHO) cells and Hi-tag was developed. MethodsFull length gerbil prestin was transfected into modified mammalian expression vectors with C-terminal 6 × His-tag. After drug selection with G418 for 4 weeks, single colonies were isolated by limiting dilution method. Cell lines highly expressing prestin were selected (named 3D5, 4D7 and 3C8). These cells were gently disrupted using a Dounce tissue grinder. Membrane fractions were extracted by ultracentrifugation and affinity chromatography was performed. The efficiency of the purification process was evaluated by quantitative Western blotting using a standard protein. ResultsAmong the cell lines constructed, Western blotting analysis showed bands at around 100 kDa and the highest intensity was confirmed from the 3C8 cell line, indicating that this cell line has the highest expression of prestin molecules. The membrane fraction was therefore extracted from this cell line and subjected to the following purification procedure. It was found that 78.7 μg of prestin was purified from 2.0 × 109 CHO cells. ConclusionIn the present study, 78.7 μg of prestin was purified from 2.0 × 109 CHO cells, which stably expressing 6 × His-tagged prestin, by extracting cell membrane fractions and standard affinity chromatography for His-tag.

Mobile-Based Digital Assessment Transforming the Learning Support Efficiency

The digital age has brought great changes in many areas of life, including education. The main change is the use of mobile device systems such as smartphones or tablets that are practical to change the way assessments affect learning efficiency. This paper examines how mobile-based digital assessment systems can improve the effectiveness and efficiency of the assessment process in learning. This paper aims to provide educators with practical guidance for integrating technology into their assessment systems by utilizing available mobile smartphones or tablets and conducting an analysis of the benefits and challenges faced. This study also explores how to maximize the use of smartphones or tablets as a support tool in conducting assessments and can enrich the learning experience of students through faster and personalized feedback. Assist educators in managing assessments in a more structured and measurable way. In addition, recommendations for the effectiveness of smartphone use in supporting the assessment system are also included so that educators can achieve optimal results in the learning process. This study contributes to the educational literature by providing new perspectives on the application of technology and the use of mobile devices to assessment by presenting empirical evidence that supports the effectiveness of assessment application in improving the quality of education.

Multi-Objective Optimization of the Surface Grinding Process for Heat-Treated Steel

This paper effectively integrates Taguchi, Response Surface Methodology (RSM), and Genetic Algorithm (GA) approaches for both single and multi-objective optimization, delivering low-cost, high-effectiveness solutions for grinding. It examines the effects of three factors—depth of cut in coarse grinding, depth of cut in fine grinding, and the number of spark-outs—on three objectives: surface roughness, grinding time, and the deviation between the desired and actual grinding depth for SKD61 steel. Through in-depth analysis, the paper describes the impact of these factors and their interactions with the responses. It also proposes the optimal parameter setup for each objective. The optimal grinding time is achieved at 950 seconds with a coarse grinding depth of 0.007 mm, fine grinding depth of 0.004 mm, and zero spark-out. The minimal deviation and surface roughness were obtained at 0 mm and 0.144 µm, respectively, using the optimal setup of a coarse grinding depth of 0.004 mm, fine grinding depth of 0.001 mm, and 10 spark-outs. By applying GA, the paper provides a Pareto solution set, offering multiple combinations of optimal factors for minimizing grinding time, deviation, and surface roughness. These solutions serve as useful references for users seeking the best trade-offs in multi-objective scenarios. This paper contributes to improving customer satisfaction by enhancing the quality and efficiency of the machining process while reducing production costs for grinding machines. Its methodology can also be applied to other optimization fields.

Utilizing Chaotic Logistic Keys for LSB1 and LSB2 Message Steganography

This paper introduces a novel hybrid data steganography method that combines the new techniques of LSB1 and LSB2. The proposed method simplifies data-hiding and extraction operations by utilizing a patch method. A unique Private Key (PK) divides the message into two parts. The first part is processed using the LSB1 method, while the second one is treated with the LSB2 method. The PK information is utilized to create secret keys, namely key1 and key2. The keys are generated by converting two Chaotic Logistic Keys (CLKs) and establishing the sequence of cover Stego bytes for concealing and revealing data. The secret message is protected within a secure key area by utilizing the PK, which improves security by preventing unwanted access. The secret message's effective extraction depends significantly on the PK's content. Any alterations to the key during the extraction step will be deemed unlawful, possibly leading to a compromized secret key. Moreover, the suggested approach is followed and assessed by using different messages. The results are comprehensively studied, ensuring a robust evaluation of the quality, efficiency, and security improvements of the data steganography process. The experimental results confirm the data steganography's quality, efficiency, and security enhancements.

A systems perspective on gaps in the person-centered sick leave and rehabilitation process: a Swedish interview study

Background Consensus on priorities to optimize the sick leave and rehabilitation process (SRP) is lacking. Objective To explore perspectives of stakeholders in the SRP on bridging the gap between desired process scope, and actual practice, from a multi-professional, multi-organizational, and interdisciplinary approach. Design and setting Focus group interviews were conducted with various SRP frontline professionals in Region Västra Götaland, Sweden, using purposive sampling to capture a range of experiences. Participants discussed their perceptions of critical changes and priorities needed to meet patients’ SRP needs in a primary care context. All interviews were analyzed using systematic text condensation, as described by Malterud. Subjects General practitioners (n = 6), rehabilitation coordinators and licensed healthcare professionals from primary healthcare (n = 13), administrators from the Social Insurance Agency, the Employment Agency and Social Services (n = 12). Results Through data analysis, the following themes emerged: 1) The need for rules and regulations to enable coherent process governance 2) Challenges and opportunities in person-centered SRP: Professional collaboration, organizational priorities, and the need for enhanced leadership, and 3) Balancing resources and patient needs in the SRP: How to improve care quality and accessibility. In summary, participants mainly discussed how to improve process efficiency and quality of care while balancing available resources and a heavy workload. A main goal was to prevent negative spirals of suboptimal decision-making in individual cases, which could lead to increased work, unfortunate outcomes, and patient suffering. Conclusions This qualitative study indicated that gaps between a desired process scope and actual practice might be bridged by enabling coherent cross-organizational process governance, prioritizing person-centered ways of working, and balancing available resources and workload. The above changes were believed to improve process quality and overall efficiency. Trial registration The study project plan was pre-registered on September 21st, 2020, in the database FOU i VGR (researchweb.org), project number 274941.

Donnan Dialysis-based Approach for Reclamation of Waste Acid with a Low Concentration

This study provides a proof-of-concept investigation into the direct utilization of low-concentration recovered acid as a proton source for the efficient recovery of organic acids via Donnan dialysis, a scenario of particular significance in industrial parks. A primary objective is to examine the implications of waste acid concentration on the coupling process. To evaluate the technological feasibility, process simulations are performed utilizing a mathematical model grounded in the Nernst-Planck equation and associated equilibrium relationships. Furthermore, a variety of experimental conditions, encompassing different types of organic acids and varying concentrations of waste acid, are explored to analyze the ion substitution behaviors involved. The findings from both simulations and experiments indicate that weaker organic acids demonstrate superior performance, particularly regarding recovery rates and process efficiency. Additionally, it is revealed that merely increasing the concentration of the draw solution does not constitute an effective approach for improving the DD-based organic acid recovery process, thereby suggesting the potential for the direct application of low-concentration recovered acid. Given its significant advantages, the proposed DD-based coupling technology shows considerable promise for future applications.

Zinc Oxide-Functionalized Hydrophobic Cellulose Nanofiber Aerogel Tailored for Eliminating Conventional Ibuprofen Tablets from Water.

A hydrophobic cellulose aerogel having water contact angle of 149⁰ was fabricated by harnessing the effect of both zinc oxide nanoflakes and stearic acid, and then employed for the elimination of emerging pharma residue Ibuprofen from water. The kinetic experimental data obtained for adsorption process was well suited with pseudo-second order kinetics. The adsorption process was well governed by Langmuir monolayer adsorption and the modified aerogel exhibited a maximum adsorption capacity of 33.52 mg/g. The hydrophobic aerogel exhibited a removal efficiency of 70 % within 60 min of contact time. Moreover, the efficiency of the adsorption process was also analyzed by altering various parameters such as pH, adsorbate dosage and adsorbent concentration. The adsorption efficiency was maximum at pH 2 and at 60 minutes of contact time. The elimination of IBP from water by the hydrophobic cellulose aerogel was assisted by hydrogen bonding, and hydrophobic-hydrophobic interaction occurred between the adsorbent and adsorbate, which was confirmed by the IR spectroscopy and SEM-EDX of the aerogels before and after the IBP adsorption process. The demonstrated work mainly emphasises assessing the practical applicability of the modified aerogel in adsorbing conventional ibuprofen (IBP) tablets directly obtained from the market rather than solely targeting the pharmaceutical ingredient and thereby, this approach could be effectively implemented at the industrial level.