Efficiency Of Production Process Research Articles

Artificial Neural Network Modeling in the Presence of Uncertainty for Predicting Hydrogenation Degree in Continuous Nitrile Butadiene Rubber Processing

The transition from batch to continuous production in the catalytic hydrogenation of nitrile butadiene rubber (NBR) into hydrogenated NBR (HNBR) marks a significant advance for applications under demanding conditions. This study introduces a continuous process utilizing a static mixer (SM) reactor, which notably achieves a hydrogenation conversion rate exceeding 97%. We thoroughly review a mechanistic model of the SM reactor to elucidate the internal dynamics governing the hydrogenation process and address the inherent uncertainties in key parameters such as the Peclet number (Pe), dimensionless time (θτ), reaction coefficient (R), and flow rate coefficient (q). A comprehensive dataset generated from varied parameter values serves as the basis for training an artificial neural network (ANN), which is then compared against traditional models including linear regression, decision tree, and random forest in terms of efficacy. Our results clearly demonstrate the ANN’s superiority in predicting the degree of hydrogenation, achieving the lowest root mean squared error (RMSE) of 3.69 compared to 21.90 for linear regression, 4.94 for decision tree, and 7.51 for random forest. The ANN’s robust capability for modeling complex nonlinear relationships and dynamics significantly enhances decision-making, planning, and optimization of the reactor, reducing computational demands and operational costs. In other words, this approach allows users to rely on a single ML-based model instead of multiple mechanistic models for reflecting the effects of possible uncertainties. Additionally, a feature importance study validates the critical impact of time and element number on the hydrogenation process, further supporting the ANN’s predictive accuracy. These findings underscore the potential of ML-based models in streamlining and enhancing the efficiency of chemical production processes.

Novel amorphous FeOOH-modified Co9S8 nanosheets with enhanced catalytic activity in oxygen evolution reaction

Efficient oxygen evolution reaction (OER) is vital for water electrolysis and advanced hydrogen energy production. However, the sluggish kinetics of this reaction require significant overpotentials, leading to high energy consumption. Therefore, developing OER electrocatalysts with exceptional performance and long-term durability is crucial for enhancing the energy efficiency and cost-effectiveness of the hydrogen production process. In this research, novel FeOOH/Co9S8 catalysts were prepared through a two-step hydrothermal reaction followed by one-step electrodeposition on nickel foam for an alkaline OER. The as-obtained catalysts possessed abundant non-homogeneous interfaces between FeOOH and Co9S8 nanosheets, conducive to optimized coordination environments of Fe and Co sites by redistributing interfacial charges. This synergy strengthened the chemisorption of oxygenated intermediates, leading to accelerated reaction kinetics, abundant active sites, and enhanced OER performance. The optimized electrocatalyst FeOOH/Co9S8-15 achieved a current density of 10 mA cm−2 at an overpotential of 248 mV and good stability for over 140 h. This study presents a novel approach for producing compelling and durable alkaline dielectric OER electrocatalysts, which will be helpful in the future manufacturing of advanced energy devices.

Facility layout design improvement in bottled drinking water at teaching factory

The Bottled Drinking Water Teaching Factory (Tefa) at State Polytechnic of Jember (Polije) produces mineral water packaged in several sizes of bottles, glasses, and gallons. The efficiency and effectiveness of the bottled drinking water production process is influenced by the layout of production facilities. The mismatch of machine layout arrangement and the number of unused equipment causes material flow to be longer. This study aims to redesign the layout of the Tefa Bottled Drinking Water facility to maximize area utilization and reduce empty space. This research provides recommendations for facility layout improvements using the Systematic layout planning (SLP) method. Alternative designs were proposed and compared the performance of each design with the initial layout. The results of the proposed method’s distance processing affect the total material handling cost. The total distance of the proposed method facility is 11.7 meters while the distance of the current facility is 28.8 meters so that can save material handling costs by 59%.

CONCEPT DESIGN FOR OPTIMIZING MASS PRODUCTION PROCESSES WITH 3D PRINTER IN THE INDUSTRY

This study deals with the conceptual design of three-dimensional (3D) printer technology for process optimization in industrial production. While initially 3D printers were primarily used for rapid prototyping, advancements in technology have transformed them into a new technology for mass production. Within the scope of this study, firstly, investigations were conducted on how Industry 4.0 technologies (internet of things (IoT), smart factories) are utilized on the production line. The first-in, first-out (FIFO) method, which is used in communication between objects (3D printer, industrial robot arm, conveyor belt, and assembly unit), has been elaborated in detail. The aim of this study is to comprehensively address and convey how Industry 4.0 technologies increase production speed and efficiency in mass production processes through the created conceptual design. In the obtained conceptual design, even though the production line has been kept limited, every stage of the production process has been thoroughly explained and examined from start to finish. Within the scope of the study, sample data of the production stages are presented with the sample software called 3D Production and Automation Software (3D MAS), which was developed using the C# programming language on Microsoft Visual Studio Community 2022 IDE. In the next study, efforts can be directed towards expanding the limited production line presented in this work, introducing other Industry 4.0 technologies, and incorporating them into the conceptual design of the production process.

Understanding cheese ripeness: An artificial intelligence-based approach for hierarchical classification

Within the contemporary dairy industry, the effective monitoring of cheese ripeness constitutes a critical yet challenging task. This paper proposes the first public dataset encompassing images of cheese wheels that depict various products at distinct stages of ripening and introduces an innovative hybrid approach, integrating machine learning and computer vision techniques to automate the detection of cheese ripeness. By leveraging deep learning and shallow learning techniques, the proposed method endeavors to overcome the limitations associated with conventional assessment methodologies. It aims to provide automation, precision, and consistency in the evaluation of cheese ripeness, delving into a hierarchical classification for the simultaneous classification of distinct cheese types and ripeness levels and presenting a comprehensive solution to enhance the efficiency of the cheese production process. By employing a lightweight hierarchical feature aggregation methodology, this investigation navigates the intricate landscape of preprocessing steps, feature selection, and diverse classifiers. We report a noteworthy achievement, attaining a best F-measure score of 0.991 through the merging of features extracted from EfficientNet and DarkNet-53, opening the field to concretely address the complexity inherent in cheese quality assessment.

Understanding of the potential role of carbon dots in promoting interspecies electron transfer process in anaerobic co-digestion under magnetic field: Focusing on methane and hydrogen production

Anaerobic digestion shows great potential for biomass energy recovery, however, the low electron transfer efficiency between microorganisms limits overall anaerobic digestion performance. In this work, aloe peel-derived carbon dots (carbon quantum dots, CQDs) were designed and investigated as accelerants for anaerobic digestion systems under an applied magnetic field to promote the interspecific electron transfer (IET) efficiency in methane and hydrogen production processes. Compared with the control group (188.9 mL/g VS, 4.2 mL/g VS, 34.7 %, and 26.3 %), the applied magnetic field (5 mT) significantly increased methane yield (256.1 mL/g VS), hydrogen yield (7.0 mL/g VS), total chemical oxygen demand reduction (41.7 %), and energy conversion efficiency (35.7 %). The introduction of the CQDs (0.18–0.45 wt.%) under a magnetic field (5 mT) has further positive effects on digestion performance. The experimental group incorporating 0.36 wt.% CQDs under a 5 mT magnetic field obtained the highest methane yield (348.9 mL/g VS), hydrogen yield (9.9 mL/g VS), and total chemical oxygen demand reduction (58.4 %), and thus the energy recovery efficiency was increased by 85 % compared with control group. The digestate incorporating 0.36 wt.% CQDs exhibited excellent thermal stability (59.71 %) and total nutrient content (38.17 g/kg), and has great potential as a main ingredient in compound fertilizer. The potential role of the magnetic field and CQDs in enhancing hydrogen interspecies electron transfer (HIET) and direct interspecies electron transfer (DIET) for methane and hydrogen production is further illustrated, providing a feasible solution for improving energy recovery from biomass.

Analysis of Research and Development in the Textile and Footwear Industry

Introduction. Rethinking the operational models of textile and footwear industry companies, transforming their organisational structures and marketing strategies, necessitates research and development to create new samples and materials that are most demanded in the new conditions. Methods. The research concept is based on the axiomatic method and system-logical analysis, the method of formalising the model of modern directions of research and development in the textile and footwear industry, the method of summarising results, the method of comparative analysis in processing economic information, and the hypothetical method of the necessity of digital design of new products using the "House of Quality" approach. Results and Discussion. Current research and development in the textile and footwear industry are directed towards creating new goods (samples, products), new technologies and equipment, and new types of production raw materials. In the textile and footwear industry, automation and digitalisation stand out as key tools for research and development, enhancing the efficiency of design and production processes, particularly for custom orders and small-scale production. Conclusion. Despite the advent of numerous innovations in textile materials, fibers, garments, and footwear, the level of research and development activities in specific companies is inadequate, which poses a significant threat to the economic security and the ability to meet societal needs for industry products. The proposed resulting indicator for research and development is the innovation saturation coefficient, which measures the efficiency of research and development expenditures. To enhance the effectiveness of the project phase in research and development within the textile and footwear industry, it is suggested to use the "House of Quality" tool.

THE USE OF DIGITAL TWINS TO IMPROVE THE OPERATIONAL EFFICIENCY IN THE EXTRACTIVE INDUSTRIES

The article examines the impact of Digital Twins on the business processes of mining companies. Mining companies can optimise their operations, from stripping and mining to enrichment and transportation. By modelling different scenarios, identifying bottlenecks and making informed decisions, companies can improve resource efficiency, reduce downtime and increase productivity, making a positive impact on their operational efficiency. Predictive maintenance strategies implemented with the help of Digital Twins help increase efficiency by minimising unexpected equipment failures and maximising uptime.The main technological effects of the use of Digital Twins in the mining industry, which affect the operational efficiency of the company, are: (1) an increase in the volume of output of commercial products after coal enrichment; (2) an increase in the coefficient of output to the production line of mining equipment.These effects lead to an improvement in the operating efficiency of the mining company, which increases indicators such as EBITDA and cash balance at the end of the period. This impact on operating efficiency is achieved by increasing the volume of commercial products, improving the efficiency of production processes, and by reducing the fixed and operating costs of the business.In order to assess the impact of the use of Digital Twin technology on mining companies, a methodology has been developed to assess the impact of the use of Digital Twin technology on mining companies. According to the results of the survey, a number of key production processes have been identified where the introduction of a Digital Twin is expected to have an impact.As a result of the empirical study, the expected increase in EBITDA from the implementation of the Digital Twin was 28%, the actual increase in EBITDA over the study period was 21%. The expected increase in free cash at the end of the period was + 593 million rubles (+130%), the actual increase in free cash for the period under study was 441 million rubles (+96%).The introduction of Digital Twins in mining companies has shown great potential for improving operational efficiency and solving complex challenges facing the industry.

Coupled Oxygen-Enriched Combustion in Cement Industry CO2 Capture System: Process Modeling and Exergy Analysis

The cement industry is regarded as one of the primary producers of world carbon emissions; hence, lowering its carbon emissions is vital for fostering the development of a low-carbon economy. Carbon capture, utilization, and storage (CCUS) technologies play significant roles in sectors dominated by fossil energy. This study aimed to address issues such as high exhaust gas volume, low CO2 concentration, high pollutant content, and difficulty in carbon capture during cement production by combining traditional cement production processes with cryogenic air separation technology and CO2 purification and compression technology. Aspen Plus® was used to create the production model in its entirety, and a sensitivity analysis was conducted on pertinent production parameters. The findings demonstrate that linking the oxygen-enriched combustion process with the cement manufacturing process may decrease the exhaust gas flow by 54.62%, raise the CO2 mass fraction to 94.83%, cut coal usage by 30%, and considerably enhance energy utilization efficiency. An exergy analysis showed that the exergy efficiency of the complete kiln system was risen by 17.56% compared to typical manufacturing procedures. However, the cryogenic air separation system had a relatively low exergy efficiency in the subsidiary subsystems, while the clinker cooling system and flue gas circulation system suffered significant exergy efficiency losses. The rotary kiln system, which is the main source of the exergy losses, also had low exergy efficiency in the traditional production process.

A Review of 3D Printing Batteries

To stabilize the Earth’s climate, large-scale transition is needed to non-carbon-emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency makes them unable to supply a constant load without storage. To address these challenges, rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance, 3D printing technology has emerged as a promising solution. This literature review provides state-of-the-art enhancements of battery properties with 3D printing, including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation, and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing battery components as well as full batteries offer design flexibility, geometric freedom, and material flexibility, reduce pack weight, minimize material waste, increase the range of applications, and have the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar.

The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

Devising a technique for measuring torque of electric motors using machine vision

The object of research is the technique for measuring torques of electric motors. The main problem to be solved is the need to expand the range of informative parameters used in transmission torque converters of electric motors. This need arises from the design complications of placing the measuring devices directly on the motor shaft. As part of the study, a system was designed for determining the torques of electric motors, which combines the procedure for assessing the load on the shaft and software tools for visually determining the quantitative characteristics of such a load using machine vision. In order to acquire visual characteristics of the twisting of the shaft, a special coupling with an insert containing a liquid was designed. This coupling is able to change its shape in proportion to the load on electric motor shaft. The proposed visual control system, based on video information processing techniques, makes it possible to analyze changes in the shape of the coupling caused by the action of the torque. Features of the application of the proposed measurement technique are that there is no need to place electronic components of the measuring transducer directly on the shaft. Due to the specified features, the proposed procedure provides a solution to the problems of torque measurement in aggressive environments, which is of crucial importance for the efficiency of some specific production processes. Analysis of the visual characteristics of torque showed that the proposed approach could be applied in measuring transducers. At the same time, the results of testing the procedure confirmed that it requires the use of high-precision video recording equipment. This paves the way for the design of new, more modern, and reliable measurement systems that could be used in a wide range of industrial solutions

IoT-Based Barcode Scanning System for Production and Warehouse Management

This project aims to develop an IoT-based barcode scanning system using an ESP32 microcontroller and a GM-65 barcode reader for efficient production and warehouse management. The system facilitates automated and accurate barcode scanning, with the collected data transmitted to a web-based platform in real-time. The web interface provides users with comprehensive insights into production quantities and warehouse status, generating a detailed journal for traceability and accountability. This innovative solution offers businesses a streamlined approach to monitor and record production activities, ultimately optimizing the overall efficiency of production and warehouse management processes.

The Impact of Changing Collaborative Workplace Parameters on Assembly Operation Efficiency

Human–robot collaborative systems bring several benefits in using human and robot capabilities simultaneously. One of the critical questions is the impact of these systems on production process efficiency. The search for high-level efficiency is severely dependent on collaborative robot characteristics and motion parameters, and the ability of humans to adjust to changing circumstances. Therefore, our research analyzes the effect of the changing collaborative robot motion parameters, acoustic parameters and visual factors in a specific assembly operation, where efficiency is measured through operation times. To conduct our study, we designed a digital twin-based model and a laboratory environment experiment in the form of a collaborative workplace. The results show that changing the motion, acoustic and visual parameters of the collaborative workplace impact the assembly process efficiency significantly.

Methanol compared to other fuels for on-board hydrogen production in thermally balanced membrane reactors with internal recycle

New processes for hydrogen production from various sources are required, which will enable decentralized hydrogen utilization. Hydrogen is a challenging energy carrier due to its low volumetric energy density, requiring compression or liquefaction, which are costly steps, reducing the overall energy transformation cycle efficiency. Thus, a hydrogen supply route based on distributed on-demand production is more attractive compared to the traditional centralized production route. In this work we analyze the use of methanol as a hydrogen carrier to be converted to hydrogen in a conceptual autothermal scaled-down system for on-board pure hydrogen production. This system integrates two concentric reactors, membrane separation and heat exchange. The required heat is supplied by catalytic combustion of the reforming effluents which are recycled internally. The temperature profile is controlled by distribution of the combustion feed. A validated mathematical model shows that the thermal efficiency of the hydrogen production process from methanol will be much higher (e.g. 45 % vs 15 % in one case, 60 % vs 30 % in another case) compared to the efficiency obtained when feeding other fuels (methane, ethanol or glycerol). In all cases full conversion of the fuel is obtained. The reason for such a significant intensification is the lack of methane formation in the reforming reactor. This leads to higher driving force for hydrogen transport through the membranes and enables lower operating temperatures, reducing heat losses to the surroundings (which are the main source for efficiency loss). The lack of methane formation is due to the Cu-based catalyst used for methanol reforming, as opposed to the Ni-based catalyst used for reforming of other fuels.

Analysis of the spatial and temporal evolution and drivers of net carbon efficiency of plantations in China

With the increasing severity of global warming, the goal of carbon sequestration and emission reduction in various industries has become an important responsibility for sustainable development. As an important part of carbon sink, the improvement of net carbon efficiency in the plantations industry will make a great contribution to the realization of the “double carbon” target in agriculture. However, there are large spatial and temporal differences in the net carbon efficiency of the plantations sector in various provinces, and there is still a large scope for increasing carbon sink and reducing carbon emission. In this paper, we have analyzed spatial and temporal trends in the net carbon sink efficiency of the plantations' production processes in 31 provinces in China (excluding Hong Kong, Macao and Taiwan) from 2000 to 2020. On this basis, we have analyzed the influencing factors of net carbon efficiency in the plantations' production process through an extended Kaya constant equation. Through the geographically and temporally weighted regression model, we explore the spatio-temporal heterogeneity of the influencing factors, and further analyze the driving and restraining factors of net carbon efficiency in plantations' production process, in order to propose appropriate countermeasures. The study shows that: (1) The net carbon efficiency of plantations sector in China increase between 2000 and 2020, and the development of net carbon efficiency is uneven across regions. (2) The geographic center of net carbon efficiency of China's plantations industry shift from the northeast to the southwest and is consistent with the migration direction of agricultural inputs. (3) The influencing factors of net carbon efficiency in the plantations sector show large regional differences, and can be specifically divided into four major types of zones. In this regard, this paper provides more appropriate countermeasure suggestions for increasing carbon sink and reducing carbon emission in each zone, with a view to achieving the goal of double carbon in agriculture with practical significance.

Efficiency of financial production process and its dependence on price anchors: Evidence from India

This paper measures the degree to which disclosed annual financial information assimilates into stock prices through the financial production process (FPP). Since it is established that price anchors impact investor trading decisions, the effect of proximity to chosen price anchors and the historical high and low prices on FPP efficiency is analysed for 611 Indian firms across 11 industries. Results show that a significant number of stocks are FPP inefficient, and the FPP efficiency is mean-reverting. Further, FPP efficiencies depend on proximity to price anchors, implying that FPP efficiency is a proxy measure of behavioural bias in stock prices.

Parametric and sensitive analysis of Pd-Ag membrane reactor performance in biogas reforming to generate decarbonized hydrogen by Computational Fluid Dynamic-Response Surface Methodology

The efficient production of hydrogen from renewable sources is pivotal for the development of sustainable energy systems. Biogas steam reforming (BSR) conducted in palladium-based membrane reactors (MRs) offers a pathway for high-purity H2 generation at lower temperatures compared to traditional processes. In this study, a computational fluid dynamics (CFD) model was developed to simulate the BSR process within a Pd-Ag MR. The CFD model has integrated comprehensive kinetics, mass transport, and hydrodynamics, and its accuracy was confirmed through validation against experimental data, demonstrating substantial agreement. As a novel approach, to optimize the BSR reaction conditions to get the best MR performance, the CFD model was coupled with response surface methodology (RSM), which has been employed to enable the identification of optimal values for key parameters such as reaction temperature, pressure, gas hour space velocity, feed molar ratio, and sweep gas ratio, where the temperature was found to have the most significant impact on the MR’s performance within the examined intervals of input parameters. The objective of RSM method applied to CFD modeling was to maximize with high accuracy CH4 conversion, H2 recovery, and reduce CO2 emissions. RSM models effectively established correlations between input parameters and responses. The determined optimal conditions for the BSR process were a temperature of 683 K, a reaction pressure of 6 bar, a GHSV of 3000 h−1, a H2O/CH4 ratio of 1.34, and a sweep ratio of 9. Under these conditions, the system achieved remarkable results with 100 % CH4 conversion, 43 % H2 recovery, and an 81 % reduction in CO2 emissions. This study underscores the effectiveness of integrating CFD with RSM for the precise modeling and optimization of MRs in BSR reactions. The synergy between these approaches provides a robust framework for advancing the efficiency and sustainability of hydrogen production processes from renewable sources.

ANALYSIS OF LABORATORY STUDIES OF HYDROPHOBIC EMULSIONS TO ENSURE WATER INSULATION WORK IN OIL FIELDS

This article is devoted to the research and implementation of methods for ensuring water insulation works (WIW) in the oil sector using hydrophobic emulsions. In the context of modern requirements for environmental safety and efficiency of production processes in the oil industry, where water can become a source of problems, the introduction of innovative materials and technologies plays an important role. Conducting laboratory studies of hydrophobic emulsions for WIW in the oil sector is a multiprocess approach, starting from the initial assessment of the chemical component and ending with thorough testing for compliance with the requirements of specific operating conditions. The analysis evaluates the physico-chemical properties of the emulsion, such as density, viscosity, pH level, degree of dispersion, as well as its adhesive and cohesive characteristics. Special attention is paid to the study of the surface properties of hydrophobic emulsions, since their ability to form stable water-repellent coatings directly affects the effectiveness of protection against water exposure. To do this, tests are carried out on wettability, contact angle, adhesion to various types of surfaces, which allows us to determine the optimal conditions for the use of emulsions in specific situations. The article describes the principles of action of hydrophobic emulsions in the context of waterproofing works, emphasizing their ability to create a protective barrier, preventing moisture penetration into oil facilities. The paper presents the results of laboratory studies demonstrating the effectiveness of the use of hydrophobic emulsions in various operating conditions. The article discusses the technical aspects of the use of hydrophobic emulsions in the WIW process, analyzes their resistance to various environmental factors, and also considers the prospects for the development of this technology in the oil industry. The results obtained can be useful for specialists dealing with water insulation in oil companies, as well as for the scientific and engineering community interested in the development of innovative technologies in the oil sector. The purpose of this article is to analyze laboratory research and provide recommendations on the use of polymer composition.

The homemade soap production: a millenary pratice that stimulates technological innovation among students

The process of producing homemade soap using leftover oils from frying makes possible the teaching-learning process of various contents of Chemistry, articulating the theory and practical through experimentation. The aim of this work is present the technological product created by a student and their workgroup during a project with the theme of the production of fragrant homemade soap. The soap mixer was developed within the scope of research developed in a professional master’s degree, during a qualitative intervention research, carried out in the first grade of the New High School of the State Education Network in the state of Espírito Santo, Brazil. This intervention had a Project-Based Learning (PBL) which teaching methodology, focused on Science, Technology, Society and Environment (STSE – CTSA in Portuguese) Although soap production is common, the need for assistance in the soap mixing process encouraged the development of a technological product, developed from a recyclable and low-cost materials. This action demonstrates that the problematic proposition by PBL focusing on STSE provided a favorable scenario for the development of skills set in students, additionally this demonstrates that innovation can emerge from routine activities, and can contribute to the efficiency of production processes. Furthermore, the use of recyclable materials in the manufacturing process of the soap whisk reinforces the commitment to sustainability and economy.