157082 research outputs found
Sort by
Impact of Emissions Mitigation Technologies on the Costs of a CO<sub>2</sub> Capture Plant for a Generic Refinery Process Flue Gas
No full textCarbon capture is a necessary tool to support the energy transition and help companies and countries reach their climate targets. Amine-based absorption is the most mature technology for postcombustion carbon capture. Understanding solvent behavior in relation to degradation and emissions is crucial for the implementation of such a process. However, this task proved to be extremely difficult as the behavior is dependent on several factors, including the solvent formulation, the flue gas composition, the capture rate, and the reboiler temperature, to cite some. This work investigates the impacts of a few selected technologies to handle emissions on the costs of implementing a capture plant in a refinery. The cases were evaluated using two solvents, 30 wt % MEA and CESAR1, where simulations were conducted to assess the process performance and size of the main plant equipment. A cost methodology was used to compare the cases where it shows that the configuration with water wash, dry bed, and acid wash was the most technically and economically efficient. Using the proposed methodology shows that operating the capture plant with CESAR1 is more economical than with 30 wt % MEA mainly due to the difference in regeneration costs (i.e., steam costs)
From Haskell to a New Structured Combinator Processor
No full textThis paper presents KappaMutor, a new graph reduction processor, along with its Haskell compiler. KappaMutor is based on structured combinators, a recently proposed combinator encoding, which is more flexible and efficient than fine-grained SKI combinators. The processor exploits parallel memories to enable single-cycle reduction of structured combinators while maintaining good compactness, utilising less than 1% of the logical resources on a modern FPGA. Its Haskell compiler implements novel code generation strategies designed to minimise combinator usage and achieve full laziness --- the first such implementation for structured combinators, to the best of our knowledge. Based on our measurements, structured combinators can reduce runtimes by 9% to 58%, compared to running equivalent SKI combinator programs on KappaMutor
Dynamic VSI Max-EWMA control chart for joint monitoring of time-between-events and amplitude in high-quality manufacturing processes
No full textControl charting methodologies for surveillance of an event frequency and amplitude are essential in manufacturing and industrial operations. There are some existing research works regarding the independent monitoring of the two values. Nevertheless, concurrently surveillance of an event’s frequency and amplitude may prove to be more practical and effective. This study introduces the maximum exponentially weighted moving average (Max-EWMA) scheme with variable sampling interval (VSI) strategy (denoted as VSI Max-EWMA scheme) for the concurrent surveillance of both the frequency and amplitude of an event. The VSI strategy enables the VSI Max-EWMA scheme to adjust its sampling interval dynamically, switching between long and short intervals using the position of the current sample. Similar to the existing Max-EWMA scheme, the VSI Max-EWMA scheme’s statistic corresponds to the maximum of the absolute values of two EWMA statistics, i.e., one for monitoring the frequency and another for tracking the amplitude of an event. Note that the frequency is expected to conform to an exponential distribution, whereas the amplitude is anticipated to adhere to a gamma distribution. The suggested approach aims to improve the speed of identifying changes in the event’s frequency and/or amplitude. The performance analysis demonstrates that the VSI Max-EWMA scheme surpasses its existing counterparts in identifying minor and medium changes in the process based on the average time to signal (ATS) criterion. Lastly, we demonstrate the utilization of the suggested scheme by using integrated circuit chips dataset in the Electrical and Electronics industry, and demand and sales data of an automotive industry. For the integrated circuit chips dataset applied in this paper, the VSI Max-EMWA scheme detects its first OC signal at the 12.5-time units, which is quicker than the Max-EWMA scheme at 17-time units
Improving Consumer Engagement in Digital Marketing Through Cognitive AI
No full textIn this rapidly evolving landscape of digital marketing, consumer engagement has become a critical determinant of brand success and customer loyalty. Traditional marketing strategies are no longer sufficient to meet the growing demand for personalized, real-time interaction. AI, natural language processing, machine learning, and predictive analytics offer powerful tools to enhance consumer engagement. By enabling deeper insights into consumer behavior, preferences, and intent, cognitive AI allows marketers to deliver more relevant and meaningful experiences across digital channels.Improving Consumer Engagement in Digital Marketing Through Cognitive AI explores the role of cognitive AI in digital marketing, focusing on intelligent systems for consumer behavior. It examines the improvement of customer experiences for higher conversion rates and improved data-driven decision-making. Covering topics such as AI, consumer behavior, and digital marketing, this book is an excellent resource for marketing professionals, business owners, researchers, and scientists
Dynamic Mechanical Properties of Polymer Dispersed–Silica Nanoparticle Composites
No full textA series of nanocomposites were prepared by dispersing various silica nanoparticles in polystyrene (PS) and poly(methyl methacrylate) (PMMA) and analyzed by differential scanning calorimetry (DSC) and dynamic mechanical thermal analysis (DMTA). Colloidally dispersed silica nanoparticles and structured fumed silica were used in the synthesis, leading to well-dispersed systems. A detailed investigation was conducted into the thermal and dynamic mechanical behavior of the nanocomposites. The findings of this study demonstrate that the incorporation of filler particles increases the glass transition temperature (Tg) and suppresses polymer flow, resulting in an extended rubbery plateau. Significant reinforcement as evidenced by an increased plateau modulus above Tg was only observed for samples containing fumed silica. While neat PMMA begins to flow and deform irreversibly above 150 °C, the fumed silica/–polymer hybrid materials remain stable up to 240 °C, exceeding the polymer’s Tg by over 100 °C. The polymer nanocomposites exhibited slight mechanical damping at high temperature as evidenced by a surprisingly low tan δ (<0.1). Compared to the structured fumed silica hybrids, colloidally dispersed silica had very slight effect on polymer reinforcement.</p
Characterization of microplastics and associated metals in green mussel cultivation: Estimation of potential health risks
No full textGreen mussels, a popular seafood in Jakarta, have been found to be contaminated with microplastics. Microplastics are hydrophobic, they can adsorb various pollutants, such as metals and persistent organic compounds, onto their surface, thereby increasing the potential for biomagnification through the trophic chain. Microplastic contamination in mussels is a growing concern and may pose health risks to consumers. This research aims to characterize the types of polymers, shape colors, abundance of microplastic, detect heavy metal contaminants on microplastic surfaces in the gills, and estimate the health risks associated with their consumption. The results showed that microplastics were detected in all 120 green mussels sampled, with fragments being the dominant type, followed by fibers and films. The average abundance of microplastics was 18 ± 9.4 particles per individual or 4 ± 2.8 per gram of wet tissue weight and the average wet weight was 4.9 ± 2.15 g. FTIR analysis identified 15 types of polymers, and polymer hazard levels led to risk categories I, II III and V, which is considered very dangerous to human health. The percentages of aluminum and lead on the surface of gill microplastics were 0.15 % and 0.01 %, respectively, while the percentage of aluminum identified in microplastics on the Whatman filter was 0.23 %. The estimated annual quantity of microplastics ingested by humans ranged from 10,192 items to 76,440 items among diverse age ranges. It is estimated that each person in Indonesia ingests 271,313 microplastics annually through the consumption of green mussels. The ingestion of microplastics also leads to the intake of associated heavy metals, posing significant risks to human health
Gas-solid hydrodynamics of Geldart D particles in a tapered fluidized bed by ECT measurement
No full textTapered fluidized beds have gained increasing attention in industrial applications due to their superior ability to process particles with wide size and density distributions, effectively alleviating issues such as incomplete mixing and excessive entrainment of fines. In this study, the gas–solid hydrodynamics of a lab-scale tapered fluidized bed were investigated using rice particles as a representative Geldart D material. A dual-plane Electrical capacitance tomography (ECT) sensor and pressure fluctuation measurements were employed to characterize flow behavior over a range of gas velocities. The results reveal that the tapered geometry induces a distinct flow structure compared to conventional cylindrical beds. Both void fraction and bubble size decreased with height, in contrast to the typical bubble growth observed in straight-sided beds. The most significant reduction occurred at the gas velocity of 3Umf, with a 47 % decrease in void fraction and an 11.7 % reduction in bubble diameter. Transient slugs occasionally appeared at the lower measurement plane but were absent at the upper plane, indicating effective suppression of slug propagation and improved flow uniformity. As the gas velocity increased from 1.5Umf to 2.5Umf, the power spectra of pressure signals shifted from broadband to narrowband with a pronounced peak at 3.8 Hz, then broadened again at 3Umf, reflecting the attenuation of slugging behavior in the tapered bed. Hilbert-Huang Transform analysis further confirmed intensified gas–solid interactions and flow regime transitions with increasing gas velocity. These findings provide new insights into the hydrodynamics of tapered fluidized beds and offer practical guidance for their design, operation, and industrial-scale application
Comparative Evaluation of Reinforcement Learning and Model Predictive Control for 6DoF Position Control of an Autonomous Underwater Vehicle
No full textAutonomous Underwater Vehicles (AUVs) require precise and robust control strategies for 3D pose regulation in dynamic underwater environments. In this study, we present a comparative evaluation of model-free and model-based control methods for AUV position control. Specifically, we analyze the performance of neural network controllers trained by three Reinforcement Learning (RL) algorithms---Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC)---alongside a Model Predictive Control (MPC) baseline. We train our RL methods in a simplified AUV simulator implemented in PyTorch, while our evaluation is done in a realistic marine robotics simulator called Stonefish. Controllers are evaluated on the basis of tracking accuracy, robustness to disturbances, and generalization capabilities. Our results show that, MPC suffers from unmodeled dynamics such as disturbances, whereas RL demonstrates adaptation capabilities to disturbances. Also, although MPC demonstrates strong control performance, it requires an accurate model, high compute power and a careful implementation to run in real-time whereas the control frequency of RL policies is only bound by the inference time of the policy network. Among RL-based controllers, PPO achieves the best overall performance, both in terms of training stability and control accuracy. This study provides insight into the feasibility of RL-based controllers for AUV position control, offering guidance for selecting suitable control strategies in real-world marine robotics applications
The spatial correlation network and driving factors of economic resilience in the construction sector: Evidence from China
No full textThe Chinese construction sector (CS) is the largest in the world in terms of total output value. Studying the economic resilience of the construction sector (ERCS) is thus of critical importance for the sustainable development of the CS globally. Regional differences in the CS economy have raised concerns about the spatial heterogeneity of the ERCS. In this study, the social network method and random forest models were used to explore the characteristics and driving factors of the ERCS spatial network. The findings reveal that the ERCS spatial network has a clear “center-edge” structure, with direct or indirect relationships across different regions as well as significant spatial spillover effects. The ERCS spatial network also exhibits a significant clustering pattern, with a high degree of internal integration and interdependence. Market size, technological innovation level, and industry scale were the main factors that promoted network formation. These insights provide a deeper understanding of the mechanisms that drive ERCS networks and offer guidance to countries aiming to develop sustainable growth strategies while also providing technical and managerial references to the global CS. This research framework thus serves as an effective complement to the sustainable development of the CS
Gas-solid hydrodynamics of Geldart D particles in a tapered fluidized bed by ECT measurement
No full textTapered fluidized beds have gained increasing attention in industrial applications due to their superior ability to process particles with wide size and density distributions, effectively alleviating issues such as incomplete mixing and excessive entrainment of fines. In this study, the gas–solid hydrodynamics of a lab-scale tapered fluidized bed were investigated using rice particles as a representative Geldart D material. A dual-plane Electrical capacitance tomography (ECT) sensor and pressure fluctuation measurements were employed to characterize flow behavior over a range of gas velocities. The results reveal that the tapered geometry induces a distinct flow structure compared to conventional cylindrical beds. Both void fraction and bubble size decreased with height, in contrast to the typical bubble growth observed in straight-sided beds. The most significant reduction occurred at the gas velocity of 3Umf, with a 47 % decrease in void fraction and an 11.7 % reduction in bubble diameter. Transient slugs occasionally appeared at the lower measurement plane but were absent at the upper plane, indicating effective suppression of slug propagation and improved flow uniformity. As the gas velocity increased from 1.5Umf to 2.5Umf, the power spectra of pressure signals shifted from broadband to narrowband with a pronounced peak at 3.8 Hz, then broadened again at 3Umf, reflecting the attenuation of slugging behavior in the tapered bed. Hilbert-Huang Transform analysis further confirmed intensified gas–solid interactions and flow regime transitions with increasing gas velocity. These findings provide new insights into the hydrodynamics of tapered fluidized beds and offer practical guidance for their design, operation, and industrial-scale application
Access Repository Dashboard
Do you manage Heriot Watt Pure? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!