Real-time Production Monitoring Research Articles

Online welding status monitoring method of T-joint double-sided double arc welding based on multi-source information fusion

In modern manufacturing, double-sided double arc (DSDA) welding brings advantages in properties and efficiency for T-shaped joints. However, the double-side forming makes it difficult to detect the imperfections and ensure weld joint quality, which few studies have covered. Most existing methods deal with single-arc welding with limited sensing sources. To fill the gap, an online welding status monitoring method for DSDA welding is proposed based on the fusion of welding current, arc voltage, arc sound, and weld pool images. In pre-processing, the automatic weld pool region of interest (ROI) detection method based on the lightweight YOLO-L model and the waveform denoising algorithm are designed. In feature engineering, waveform signals are analyzed in both the time and frequency domain. A weld pool feature extractor based on a convolutional neural network (CNN) is proposed with good effectiveness and interpretability. The output feature combination is refined by Fisher-based selection and evaluation. In model building, an ensemble learning model based on three high-fit basic learners is proposed, with an accuracy of 98.538 %. The proposed model shows significant advantages over the single basic classifier and other ensemble methods. Experiments verify high precision and robustness, laying a foundation for accurate real-time monitoring of DSDA welding production.

Development of DNA aptamers universally bound to single-chain fragment variables and their applications in bioprocess monitoring

Single-chain fragment variables (scFvs), composed of variable heavy and light chains joined together by a peptide linker, can be produced using a cost-effective bacterial expression system, making them promising candidates for pharmaceutical applications. However, a versatile method for monitoring recombinant-protein production has not yet been developed. Herein, we report a novel anti-scFv aptamer-based biosensing system with high specificity and versatility. First, anti-scFv aptamers were screened using the competitive systematic evolution of ligands by exponential enrichment, focusing on a unique scFv-specific peptide linker. We selected two aptamers, P1-12 and P2-63, with KD = 2.1 μM or KD = 1.6 μM toward anti-human epidermal growth factor receptor (EGFR) scFv, respectively. These two aptamers can selectively bind to scFv but not to anti-EGFR Fv. Furthermore, the selected aptamers recognized various scFvs with different CDRs, such as anti-4-1BB and anti-hemoglobin scFv, indicating that they recognized a unique peptide linker region. An electrochemical sensor for anti-EGFR scFv was developed using anti-scFv aptamers based on square wave voltammetry. Thus, the constructed sensor could monitor anti-EGFR scFv concentrations in the range of 10–500 nM in a diluted medium for bacterial cultivation, which covered the expected concentration range for the recombinant production of scFvs. These achievements promise the realization of continuous monitoring sensors for pharmaceutical scFv, which will enable the real-time and versatile monitoring of large-scale scFv production.

Advancing Manufacturing Efficiency through Real-time Production Monitoring and Control Systems

The advantages of both real-time production monitoring and control systems in enhancing the advancement of manufacturing efficiency cannot be overlooked. Manufacturing industries need to meet customers’ demand while still maximizing their profits via avoiding emergency shutdown of plants during production. Nonetheless, quality products with high purity level should be supplied uninterruptedly without breakage of supply chain. In recent times, failure of many of manufacturing industries to deliver their products to end users have been cited and reported. This was due to inadequate scheduling which was not being monitored via real-time production and control systems. There is need to tackle this problem which calls for the adoption of real-time production monitoring and control systems. In this paper, the concepts behind the use of real-time production monitoring and control systems were discussed. Consideration was given to how real-time production monitoring can be integrated in manufacturing. The features of real-time control systems were discussed alongside with their industrial applications in various disciplines of Engineering. Data analysis and prognostics, data collection, visualization module and data storage were identified as the relevant sequential steps in real time production monitoring in advancing manufacturing efficiency. In conclusion, the significance and importance of real-time production monitoring and control systems in advancing manufacturing efficiency have been revealed and discussed in this paper.

HARNESSING DATA ANALYTICS FOR SUSTAINABLE BUSINESS GROWTH IN THE U.S. RENEWABLE ENERGY SECTOR

The U.S. renewable energy sector is at the forefront of addressing global environmental challenges, with a growing emphasis on sustainability and clean energy alternatives. This review explores the pivotal role of data analytics in fostering sustainable business growth within this dynamic industry. As renewable energy sources continue to gain prominence, leveraging data analytics becomes imperative for companies aiming to optimize operations, enhance efficiency, and contribute to long-term ecological objectives. Data analytics serves as a powerful tool for harnessing insights from vast and complex datasets generated by renewable energy systems. This review delves into the ways in which organizations can extract actionable intelligence from data, enabling informed decision-making and strategic planning. From predictive maintenance of renewable infrastructure to real-time monitoring of energy production, data analytics empowers businesses to streamline processes and mitigate operational risks. Furthermore, the review sheds light on the role of data analytics in promoting innovation and research within the U.S. renewable energy sector. By analyzing patterns, trends, and historical data, organizations can identify areas for technological advancements, thereby accelerating the development of cutting-edge solutions. This proactive approach not only contributes to sustainable business growth but also positions the sector as a driver of innovation in the broader landscape of clean energy technologies. The review also highlights the challenges and opportunities associated with the integration of data analytics in the renewable energy domain, considering factors such as data security, regulatory compliance, and the need for skilled professionals. The synthesis of data-driven insights with sustainability objectives not only enhances the competitiveness of businesses in the U.S. renewable energy sector but also aligns them with global efforts to address climate change and achieve a more sustainable energy future. 
 Keywords: Data, Business, USA, Renewable Energy, Sustainability, Review.

Enhancing virtual flow metering on offshore oil platforms through parallel computing and data reconciliation

Technological advances in recent years have facilitated data accessibility and increased computational resources; consequently, more remarkable numerical performances in data analysis and computational tests have become possible. In this context, parallel computing techniques are considered relevant tools in applications necessitating reduced computational time, such as online and real-time monitoring of industrial processes. For these reasons, the present work develops and implements online and real-time monitoring of offshore oil and gas production, using parallel computing to improve the performance of the data reconciliation methodology implemented, which will estimate the virtual flow metering with more reliability and in real time. To achieve this, three optimization methods (DA, L-BFGS-B, and SLSQP) and two approaches of parallelization schemes were considered: (1) parallelization of the simulations of each production well; (2) parallelization of the numerical gradient, performed by the deterministic optimization method. Parallel implementations allowed the reduction of up to 80% (type 1) and up to 89% (type 2) of the simulation time compared to their serial counterparts. Hence, the parallel execution of type 2 exhibited more promising results, while type 1 demonstrated greater adaptability to optimization methods. Finally, the results indicate that parallel computing techniques facilitate data reconciliation in real-time, enabling online monitoring in a much shorter time and more accurately than achieved with similar sequential procedures. Moreover, the implemented methodology has proven effective for virtual flow metering of each well’s flow rates of oil, water, and gas. In this context, parallelism techniques present the potential for developing the global monitoring of the virtual flow meters for real-time oil, water, and gas flow rates in each well.

IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.

Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics

Over the next decades, solar energy power generation is anticipated to gain popularity because of the current energy and climate problems and ultimately become a crucial part of urban infrastructure. Furthermore, in order to facilitate the rapid deployment of solar production in sustainable cities, solar technologies must be digitalized and smart. This will increase their reliability and ensure their optimal performance. This work fits into this perspective and focuses on the effectiveness of processing data using the Digital Twin (DT) framework as a state-of-art information technology for urban distributed solar PV systems. Accordingly, the present paper deals with the DT of five Building-added Photovoltaic (BAPV) systems installed at the solar village in Benguerir city, Morocco. The 3D models were generated in the Rhinoceros platform, the solar AC electrical production of each photovoltaic system is calculated by the ladybug-plugin-integrated statistical model, within the grasshopper environment. Additionally, data assessment and visualization are processed through Python. Then, a hybrid model-based and data-driven fault detection and diagnosis (FDD) approach is proposed to identify and isolate anomalies for decentralized solar PV systems at the urban scale using monitoring and inspection techniques, namely Remote Sensors (RS) and real-time solar production monitoring system. The application of the DT concept for complex dynamic systems has shown its effectiveness in ensuring optimal operating conditions for the energy systems by measuring the spatiotemporal energy performance of distributed solar PV systems at an urban scale, evolving with the changing environment, and identifying degradation based on anomalies occurrence; Only then when the physical models need to be amended. Therefore, the main goal behind this work is to advance knowledge regarding research on condition monitoring of solar systems in cities while considering issues related to anomalies like the degradation phenomenon in PV modules over time. The findings of this study have proved that the state of health and monitoring of PV systems could be successfully detected and efficiently monitored using the combination of real time monitoring and remote aerial sensing.

Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning-based image segmentation

Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick biomass harvest and screening promising isolates. Here, a deep-learning-based segmentation model was developed for automatic detection and segmentation of LDs using the model yeast species Lipomyces starkeyi, leading to fast and accurate quantification of lipid contents in liquid cultures. The trained model detected the yeast's cell and LDs in light microscopic images with an accuracy of 98% and 92%, respectively. Lipid content prediction using pixel numbers counted in segmented LDs showed high similarity to lipid quantification results obtained with gas chromatography-mass spectrometry. This automated quantification can highly reduce cost and time in real-time monitoring of lipid production, thereby providing an efficient tool in bio-fermentation.

Real-time monitoring strategies for optimization of in vitro transcription and quality control of RNA.

RNA-based therapeutics and vaccines are opening up new avenues for modern medicine. To produce these useful RNA-based reagents, in vitro transcription (IVT) is an important reaction that primarily determines the yield and quality of the product. Therefore, IVT condition should be well optimized to achieve high yield and purity of transcribed RNAs. To this end, real-time monitoring of RNA production during IVT, which allows for fine tuning of the condition, would be required. Currently, light-up RNA aptamer and fluorescent dye pairs are considered as useful strategies to monitor IVT in real time. Fluorophore-labeled antisense probe-based methods can also be used for real-time IVT monitoring. In addition, a high-performance liquid chromatography (HPLC)-based method that can monitor IVT reagent consumption has been developed as a powerful tool to monitor IVT reaction in near real-time. This mini-review briefly introduces some strategies and examples for real-time IVT monitoring and discusses pros and cons of IVT monitoring methods.

Continuous Monitoring of Entropy Production and Entropy Flow in Humans Exercising under Heat Stress.

Complex living systems, such as the human organism, are characterized by their self-organized and dissipative behaviors, where irreversible processes continuously produce entropy internally and export it to the environment; however, a means by which to measure human entropy production and entropy flow over time is not well-studied. In this article, we leverage prior experimental data to introduce an experimental approach for the continuous measurement of external entropy flow (released to the environment) and internal entropy production (within the body), using direct and indirect calorimetry, respectively, for humans exercising under heat stress. Direct calorimetry, performed with a whole-body modified Snellen calorimeter, was used to measure the external heat dissipation from the change in temperature and relative humidity between the air outflow and inflow, from which was derived the rates of entropy flow of the body. Indirect calorimetry, which measures oxygen consumption and carbon dioxide production from inspired and expired gases, was used to monitor internal entropy production. A two-compartment entropy flow model was used to calculate the rates of internal entropy production and external entropy flow for 11 middle-aged men during a schedule of alternating exercise and resting bouts at a fixed metabolic heat production rate. We measured a resting internal entropy production rate of (0.18 ± 0.01) W/(K·m2) during heat stress only, which is in agreement with published measurements. This research introduces an approach for the real-time monitoring of entropy production and entropy flow in humans, and aims for an improved understanding of human health and illness based on non-equilibrium thermodynamics.

Dynamic Production Tracking and Color-Coded Dashboard for Manufacturing Equipment

Abstract: This project presents a real-time production monitoring and color indication dashboard for industrial machinery. The system addresses the issue of manually monitoring the quantity and quality of production of each machine in a factory and provides an automated solution that reduces manual errors and improves efficiency. The objective of this project is to implement automation to reduce the manual effort of constantly updating the status of machines and providing accurate and real-time updates to the factory manager.

Development of two devices for high-throughput screening of ethanol-producing microorganisms by real-time CO2 production monitoring.

Bioethanol's importance as a renewable energy carrier led to the development of new devices for the high-throughput screening (HTS) of ethanol-producing microorganisms, monitoring ethanol production, and process optimization. This study developed two devices based on measuring CO2 evolution (an equimolar byproduct of microbial ethanol fermentation) to allow for a fast and robust HTS of ethanol-producing microorganisms for industrial purposes. First, a pH-based system for identifying ethanol producers (Ethanol-HTS) was established in a 96-well plate format where CO2 emission is captured by a 3D-printed silicone lid and transferred from the fermentation well to a reagent containing bromothymol blue as a pH indicator. Second, a self-made CO2 flow meter (CFM) was developed as a lab-scale tool for real-time quantification of ethanol production. This CFM contains four chambers to simultaneously apply different fermentation treatments while LCD and serial ports allow fast and easy data transfer. Applying ethanol-HTS with various yeast concentrations and yeast strains displayed different colors, from dark blue to dark and light green, based on the amount of carbonic acid formed. The results of the CFM device revealed a fermentation profile. The curve of CO2 production flow among six replications showed the same pattern in all batches. The comparison of final ethanol concentrations calculated based on CO2 flow by the CFM device with the GC analysis showed 3% difference which is not significant. Data validation of both devices demonstrated their applicability for screening novel bioethanol-producer strains, determining carbohydrate fermentation profiles, and monitoring ethanol production in real time.

Polyoxometalate-cyclodextrin supramolecular entities for real-time in situ monitoring of dopamine released from neuroblastoma cells

Optimized and sensitive biomarker detection has recently been shown to have a critical impact on quality of diagnosis and medical care options. In this research study, polyoxometalate-γ-cyclodextrin metal-organic framework (POM-γCD MOF) was utilized as an electrocatalyst to fabricate highly selective sensors to detect in-situ released dopamine. The POM-γCD MOF produced multiple modes of signals for dopamine including electrochemical, colorimetric, and smartphone read-outs. Real-time quantitative monitoring of SH-SY5Y neuroblastoma cellular dopamine production was successfully demonstrated under various stimuli at different time intervals. The POM-CD MOF sensor and linear regression model were used to develop a smartphone read-out platform, which converts dopamine visual signals to digital signals within a few seconds. Ultimately, POM-γCD MOFs can play a significant role in the diagnosis and treatment of various diseases that involve dopamine as a significant biomarker.

A deep learning method for intelligent decision-making in enterprise management based on the Internet of Things

With the development and progress of various sensors, the Internet of Things technology has continued to penetrate into people’s lives. Successful application cases of the Internet of Things technology have been seen in various industries. Some scholars have begun to try to apply the Internet of Things technology to the management of various industries. Deep learning technology belongs to the category of artificial intelligence technology. Its main goal is to combine a large number of training samples to train the trained object into an object that can learn sample patterns independently. At present, deep learning has a large number of applications in various intelligent operations and key intelligent recognition fields. Due to the characteristics of its learnable training set, deep learning techniques are often used in the direction of decision analysis. The research adopts a combination of questionnaire survey and modeling analysis to explore the construction of in-depth learning methods for enterprise management intelligent decision-making in the context of the Internet of Things. The research was conducted by telephone interview and online questionnaire, and was supported by nearly 100 technical backbone personnel from 25 large enterprises. The research finds that applying the Internet of Things and deep learning technology to enterprise intelligent management decisions can improve the decision-making accuracy of enterprises, and also provide enterprises with more significant decision-making analysis advantages in the industry competition. This paper first describes the concepts of the Internet of Things, deep learning and intelligent decision-making of enterprise management. Then, in order to sort out the problems existing in the current intelligent decision-making of enterprise management, the author conducted interviews and questionnaires. Through the analysis of the questionnaire data, he found the problems existing in the current intelligent decision-making of enterprise management. Finally, he designed a new strategy of intelligent decision-making of enterprise management based on the Internet of Things and deep learning technology, through the simulation and subsequent investigation and analysis of the newly designed strategy. It is found that the enterprise management intelligent decision-making method designed by combining the Internet of Things and deep learning technology is superior to the traditional enterprise decision-making method in all aspects. The design of the system has been reformed mainly from the aspects of human resource management, digital file management and real-time monitoring of production and operation, so that the popularity of the enterprise’s comprehensive information management has reached 84.2%, the efficiency of intelligent decision-making increased by 28.7% on average. The achievements of the research not only prove the role of IoT technology in promoting enterprises’ in-depth learning, but also put forward new ideas for enterprises’ scientific decision-making.

Technology Focus: Production Monitoring (March 2023)

The oil and gas industry continues to face challenges. We have witnessed volatility in commodity prices (oil supply outstripping demand, leading to overall market slowdown), the COVID-19 pandemic affecting production and supply chains around the world, labor shortages, facilities-security issues, and the need to prepare for the energy transition, just to name a few. Operators, now more than ever, need to make the most of their assets, not only maintaining production but also understanding what is happening around the clock to be able to prevent potential threats or situations that may escalate and affect operations. New sensors and innovative uses of existing technology are allowing access to a variety of data that can be incorporated into integrated work flows and modeling tools for better understanding of the production system. There are initial signs of operations moving toward the use of real-time models supporting decision making, enabling fast response to external changes, and improved forecasting, resulting in overall improved asset performance. New technology and innovation in production monitoring is focusing more and more on data integration and the use of integrated work flows. Significant attention is being paid to leveraging digital technology (e.g., remote operations, advanced data analysis tools) and improving the accuracy of measured data for better surveillance planning aiming at efficiency gains. Some examples of advances in the past year include the following: - Successful integration of data from permanent reservoir monitoring systems into reservoir models, allowing a better understanding of reservoir changes in waterflooding applications, showing the value of 4D data - Demonstration of the value of using fiber optics (e.g., for distributed acoustic and distributed temperature sensing) for a variety of use cases including well-flow profiling, calibration of fracture propagation models, and understanding well interference - New sensors to monitor pressure and temperature beyond well-integrity barriers in zones with reduced clearance, just ready for field testing - Use of traditional acquired data (e.g., pressure) to infer inter- and intrazone well interference, affordable integration of tracers with sleeve technology for completion and reservoir diagnostic in horizontal wells - Digital methods such as machine learning and artificial intelligence for data interpretation and forecasting and even to serve as surrogate measurements to provide multiphase flow data (i.e., virtual flowmeters). Recommended additional reading at OnePetro: www.onepetro.org. SPE 210270 Innovative Applications of Oil-Soluble Tracers in Unstimulated and Stimulated Multistage Horizontal Wells for Long-Term Flow Assurance and Production Monitoring by Venky Ramanathan, Torxen Energy, et al. URTeC 3723767 Virtual Flow Metering for Continuous Real-Time Production Monitoring of Unconventional Wells by Torgeir Vanvik, Turbulent Flux, et al. SPE 204662 Artificial Intelligence and Data Analytics for Virtual Flow Metering by Anton Gryzlov, Aramco Innovations, et al.

Porous-Pt-CuO-driven electrochemical biosensor for tracking of endogenous H2S biomessenger molecules

Hydrogen sulfide (H2S) is considered to be the third endogenous gas messenger after nitric oxide and carbon monoxide, and is closely related to physiological and pathological functions of human being. Rapid and real-time monitoring of endogenous H2S production in living cells is of great significance for clinical diagnoses of related disease and in-depth investigation of pathological processes. The application of electrochemical sensors has become a promising method to achieve real-time H2S monitoring comparing to other methods. Up to now, low-cost, and highly sensitive electrocatalysts for H2S detection are conspicuously scarce. Herein, we developed a high-efficient method to construct a H2S electrochemical sensor, which configure dissolved–H2S–sensitive electrochemical sensor using porous Pt-modified copper oxide (Pt–CuO) as electrocatalyst. The obtained sensor possessed remarkable H2S detection performance, achieving the detection limit of 0.5 ppm and apparent selectivity toward various gases (e.g., ammonia, nitrogen dioxide, and carbon dioxide). The electrochemical sensor was also applied for monitoring H2S released from mesenchymal stem cells. The enhanced sensing performance was attributed to: (1) electrocatalytic reaction between CuO and [H2S] (H2S and HS−) was observed in the cyclic voltammetry curves with oxidation current peaks; (2) Pt catalytic activity promoted electron transfer during the detection process and further decreasing Pt–CuO resistance; and (3) enhanced diffusion of detection molecules/ions, which increased the reactivity sites through forming cross porous structures using sodium carbonate as sacrifice template. The proposed Pt–CuO-based electrochemical sensor shows great potential for applications in clinical diagnoses, and provides effective means to understand the role of H2S in biological processes.

An improved signal detection algorithm for a mining-purposed MIMO-OFDM IoT-based system

<abstract> <p>The coal mine internet of things (IoT) communication system is used for real-time monitoring of mining production to ensure the safety and reliability of personnel and equipment in the mine. To eliminate multipath fading in the process of wireless communication in mines, multiple-output multiplexing (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies are introduced. In this paper, a wireless communication system architecture of IoT in mining based on MIMO-OFDM is constructed. Aiming to solve the problems of intersymbol interference and frequency selective fading at the receiver, an improved minimum mean square error ordered successive interferences cancellation (MMSE-OSIC) signal detection algorithm is proposed. First, the signal-to-interference plus noise ratio of the received signal is calculated and the calculation results are sorted. The lowest signal-to-noise ratio is selected as the weakest signal layer. Then, the MMSE-OSIC algorithm is used to extract all of the signals, except the weakest layer. Finally, a maximum likelihood (ML) algorithm is used to traverse the whole signal domain; the signal symbol with the smallest distance from the weakest signal layer is found as the original signal of the weakest signal layer, and it is combined with the signal detected by MMSE-OSIC; then, the final signal detection result is obtained. The simulation results show that, compared with three benchmark algorithms, the proposed MMSE-OSIC algorithm has better signal detection performance under the conditions of different modulation methods and different channel numbers.</p> </abstract>

Silixa Chief Expects CCS Boom To Create “Huge Opportunity” for Fiber Optics

_ Fiber optics represents one of the biggest technological success stories in the recent history of the oil and gas industry. A single fiber-optic cable installed outside the casing, inside the tubing, can deliver a wealth of data over a well’s life cycle. It can sense minute shifts in acoustics, temperature, and has recently been developed to detect changes in near-wellbore strain—all prized clues as to what is helping or hurting productivity. But less than a decade ago, the ultrathin glass cables were considered exotic, and only a handful of first adopters dared to deploy them downhole. The sensing technology has, of course, gone on to become a mainstay of subsurface diagnostic programs for operators working across the planet. Now, with their skills sharpened in the upstream industry, many fiber-optics developers are gearing up for what appears to be a coming boom in carbon capture and storage (CCS) capacity. Among them is UK-based Silixa. Founded in 2007, Silixa’s innovations have improved on the spatial resolution of fiber measurements and enabled real-time monitoring of fracture growth and production. Last year, the company installed the world’s first subsea distributed acoustic sensing (DAS) system for BP to enable 4D seismic acquisition in the Gulf of Mexico. Glynn Williams, CEO of Silixa, shares in this Q&A how all this experience points to fiber having a major role to play in the rise of the CCS sector—especially when it comes to safety and regulatory matters. His background features more than 30 years spent in the upstream industry’s data acquisition arena. Williams also holds a degree in mining engineering from the University of Wales and is a member of SPE. Where do you see the carbon sequestration sector headed—is an inflection point coming? This now consumes a lot of my time. We see CCS as a huge opportunity because we’ve been engaged with the sector for a long period of time. For the type of solutions that we deliver, we can extract perhaps $1 of value for each ton per year of CO2 that is stored. When you look at the projections by the IEA, which suggest we may be storing over 7.5 gigatons of CO2 by 2050, that relates to $7.6 billion of value. That’s if we were to be successful in securing all of that—there will be other players alongside us. But to this point, we’ve provided services to 17 CCS sites going back as early as 2012. So, we’ve got a track record with our environmental business unit that serves CCS, and we’re seeing in the current period a twofold increase in pipeline opportunities. We feel that particular business unit will account for 40% of our overall pipeline in 2023.

Implementation of Arduino-Based Counting System for Kuih Ros Production Yield

Two technologies that have the potential to move the country's agro-food sector ahead are mechanization and automation. The technology's implementation will provide a consistent supply of high-quality products while lowering production costs, improving production rates, and lowering labour costs. In accordance with the current demand, this research aims to develop a counting system of Kuih Ros production which will be the extension of the Kuih Ros production machine. The system proposed a counting system to keep track the production rate of Kuih Ros before dispensed out. It involved the control of observation systems that ease product yield monitoring. The system is implemented using an infrared sensor (IR) which is embedded at the end of Kuih Ros machine dispenser for counting means. The liquid crystal display (LCD) will show the current amount and all the information regarding the total product gathered in Arduino. The hardware components required are IR sensor, LCD display and an Arduino microcontroller. Therefore, it provides provision for real-time monitoring of production by automatic synchronization of the system, which eases the counting process while reducing human error due to operation does not require human intervention. The results show the system is successfully developed in comparison to manual counting.

A Textile Manufacturing Information System with Exponential Smoothing Method

The traditional textile industry is characterized by a discrete production pattern, because the manufacturing workshop and equipment are quite old, and the level of information construction is low. To assist textile enterprises in improving the construction of informatization, the utilization of production data, and production management efficiency, the textile manufacturing information system must strengthen data integration. In this paper, a textile manufacturing information system with the exponential smoothing method is proposed. The proposed textile manufacturing information system is to assist managers in making decisions, analyzing data, and providing a convenient platform for sharing information. It uses MySQL and MongoDB dual databases as data storage and uses Apache servers as web services. The front end uses Echarts technology to complete the data visualization. According to the shortcomings in its production management process, the following modules are designed around the needs of the enterprise: production management, equipment management, employee management, statistical information, and basic information maintenance. It also connects with the front-end data acquisition system and presents the data in a visual form to achieve real-time data monitoring of production. Furthermore, the proposed information system uses a single exponential smoothing method to predict production. From the experimental results, the single exponential smoothing method can well predict the trend of textile production output in a short term, and the results are more accurate than other approaches.