Simulation of a Multistaggered Baffle Scrubber to Enhance the Efficiency of Simultaneous Desulfurization and Denitrification.

In this study, we conduct simulation research on simultaneous desulfurization and denitrification in a multistaggered baffle spray scrubber. By employing two-phase flow simulations within the Euler-Lagrange framework and calculating the gas-liquid mass transfer rate with user-defined functions, we comprehensively analyzed the effects of various operational parameters. Initially, we validated our simulation model by comparing the simulation results with experimental data. Under conditions of a 0.2 mm droplet diameter, a liquid-to-gas ratio (L/G) of 12 L/m3, and a gas flow rate of 5 CMM using a full cone nozzle, the simulation indicated a desulfurization efficiency of 99.90 versus 99.84% obtained experimentally and a denitrification efficiency of 92.01 versus 90.67% obtained experimentally. This comparison confirmed the reliability of the simulation model. Our findings indicate that a droplet size of 2 mm is optimal, enhancing the desulfurization efficiency from 99.90 to 99.98% and the denitrification efficiency from 92.01 to 99.76%. However, when the droplet size exceeds 2 mm, efficiencies marginally decrease. Increasing the liquid-to-gas ratio to 16 L/m3 further improves desulfurization and denitrification efficiencies to 99.98 and 99.80%, respectively. In contrast, higher inlet flue gas flow rates reduce these efficiencies, with a decline observed from 100% to as low as 93.90% for denitrification with 2 mm droplets. Additionally, the use of a swirl cone nozzle, compared to full or hollow cone nozzles, better disperses droplets, enhancing the gas-liquid contact and achieving efficiencies of 99.99% for desulfurization and 99.81% for denitrification with 2 mm droplets. These insights are valuable for optimizing operational conditions in industrial-scale spray scrubbers, significantly contributing to mitigating the environmental impacts of industrial emissions.

Effect of Operating Conditions on the Gasification of Pet-Coke Water Slurry in an Entrained-Flow Gasifier Simulation.

Petroleum coke, commonly known as pet-coke, represents a promising and cost-effective alternative fuel source, produced as a byproduct of large-scale heavy crude oil refining. This study first simulated the gasification process of pet-coke slurry using a three-dimensional computational fluid dynamics (CFD) approach based on the Eulerian-Lagrangian method. The simulation was carried out in a 2-ton-per-day (2TPD) entrained-flow gasifier, aiming to optimize the production of hydrogen (H2) and carbon monoxide (CO) as synthetic gases. This study investigated the effects of operational parameters, including the oxygen/slurry ratio and moisture content in the slurry, on various aspects such as fluid dynamics, temperature distribution, particle trajectories, carbon conversion, and gas composition within the pet-coke slurry gasifier. The base conditions of the simulation were meticulously cross-validated with high-precision experimental data. The results indicated that higher oxygen/slurry ratios led to increased concentrations of carbon dioxide (CO2) and decreased fractions of H2, primarily due to the prevalence of the reverse water-gas shift reaction. Moreover, raising the moisture content in the pet-coke slurry led to decreased CO levels and enhanced production of H2 and CO2, triggered by the activation of the forward water-gas shift reaction. These results underscore the potential of pet-coke slurry as a favorable feedstock for syngas production and the achievement of carbon neutrality through the careful optimization of operational conditions. Our findings provide valuable insights for further experimental exploration and the development of practical applications for pet-coke gasification.

Burst and Memory-aware Transformer: capturing temporal heterogeneity.

Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model, designed to explicitly address temporal heterogeneity. The BMT model embeds the burstiness and memory coefficient into the self-attention module, enhancing the learning process with insights derived from the bursty patterns. Furthermore, we employed a novel loss function designed to optimize the burstiness and memory coefficient values, as well as their corresponding discretized one-hot vectors, both individually and jointly. Numerical experiments conducted on diverse synthetic and real-world datasets demonstrated the outstanding performance of the BMT model in terms of accurately predicting event times and intensity functions compared to existing models and control groups. In particular, the BMT model exhibits remarkable performance for temporally heterogeneous data, such as those with power-law inter-event time distributions. Our findings suggest that the incorporation of burst-related parameters assists the Transformer in comprehending heterogeneous event sequences, leading to an enhanced predictive performance.

Explainable ML models for a deeper insight on treatment decision for localized prostate cancer.

Although there are several decision aids for the treatment of localized prostate cancer (PCa), there are limitations in the consistency and certainty of the information provided. We aimed to better understand the treatment decision process and develop a decision-predicting model considering oncologic, demographic, socioeconomic, and geographic factors. Men newly diagnosed with localized PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results Prostate with Watchful Waiting database were included (n = 255,837). We designed two prediction models: (1) Active surveillance/watchful waiting (AS/WW), radical prostatectomy (RP), and radiation therapy (RT) decision prediction in the entire cohort. (2) Prediction of AS/WW decisions in the low-risk cohort. The discrimination of the model was evaluated using the multiclass area under the curve (AUC). A plausible Shapley additive explanations value was used to explain the model's prediction results. Oncological variables affected the RP decisions most, whereas RT was highly affected by geographic factors. The dependence plot depicted the feature interactions in reaching a treatment decision. The decision predicting model achieved an overall multiclass AUC of 0.77, whereas 0.74 was confirmed for the low-risk model. Using a large population-based real-world database, we unraveled the complex decision-making process and visualized nonlinear feature interactions in localized PCa.

Numerical Optimization on Char Conversion and NOx Emission under Various Operating Conditions in a Retrofit Biomass Boiler.

Retrofitting retirement or existing fossil boiler with biomass is an important method of curbing electricity shortage and lowering the cost of modern power plants. However, the use of biomass combustion is hampered by operational problems, such as the resulting high unburned carbon, amount of bottom ash, and nitrogen oxide (NOx) release. In this study, we investigated the burning of pulverized biomass in a retrofitting boiler power plant using computational fluid dynamics of commercial software fluent ANSYS to determine the optimal combustion conditions. The objective of this study was to investigate a 125 MWe pulverized biomass boiler that was retrofitted from an anthracite down-fired boiler. The air distribution, including the influence of the secondary air ratio and the location of the burner standby, was evaluated. Key factors such as biomass ash mass at the hopper, char conversion, and high zone temperature relating to NOx formation/reduction were calculated. The adjustment of the secondary air ratio from 30 to 50% of the total air and the mass ash at the hopper significantly decreased to a low value at 247 kg/h and a high value of char conversion at 97.33% in case R (SA40%). The standard deviation temperature was 240 K at the BNR B-A level for case R, which was significantly lower than in other cases. This implies that the best mixing of air and biomass occurs in case R at 40%. Comparative analysis of the burner standby conditions showed that the NOx emission was similar at the boiler outlet (approximately 94-116 ppm). Burner A on standby, with a secondary air ratio of 40%, was used as the optimal case with the highest value of char conversion at 98.43%, the lowest bottom ash release of 204 kg/h, and a low-NOx emission of 106 ppm.

Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as machine-challenging tasks (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances? We hypothesized that a learning mechanism replicating the human brain is effective for tasks where machine intelligence is difficult. Multiple experiments corresponding to specific types of MCTs where machine intelligence has room to improve performance were performed using predictive coding, a more biologically plausible learning algorithm than backpropagation. This study regarded incremental learning, long-tailed, and few-shot recognition as representative MCTs. With extensive experiments, we examined the effectiveness of predictive coding that robustly outperformed backpropagation-trained networks for the MCTs. We demonstrated that predictive coding-based incremental learning alleviates the effect of catastrophic forgetting. Next, predictive coding-based learning mitigates the classification bias in long-tailed recognition. Finally, we verified that the network trained with predictive coding could correctly predict corresponding targets with few samples. We analyzed the experimental result by drawing analogies between the properties of predictive coding networks and those of the human brain and discussing the potential of predictive coding networks in general machine learning.

Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis.

The basic properties of coal influence various procedures of power generation, such as the design of power generation plants, estimation of the current condition of boilers, and total efficiency of power plants. The elemental composition is a needed factor in evaluating the process of chemical conversion and predicting the flow of flue gas and the quality of air in coal combustion. In the past, several relationships have been established using ultimate and proximate analyses. This study aims to predict the elemental compositions of 104 thermal coals used for coal-fired power plants in South Korea using an artificial neural network (ANN) that uses proximate analysis values as input parameters. The ANN-based model was optimized with six activation functions and four hidden layers after evaluating various performance indices, including R 2, mean square error (MSE), and epoch, then additional calculations were derived to compare performances from previous research using the mean absolute error (MAE), average absolute error, and average bias error. It was found that the best topology was established using the Levenberg-Marquardt activation function and 10 hidden layers, resulting in the highest R 2 value and smallest MSE of all topologies tested. As a result, the relative impact on calculation accuracy was derived from ANN hidden layers to analyze prediction accuracies of carbon, hydrogen, and oxygen compositions. Accuracy was improved over previous results by 4.71-0.91% via coal rank division topology optimization. Based on the MAE, the current results are even close in performance to those of adaptive neuro-fuzzy inference systems. They also outperformed previous research models by 5.40 and 7.39% in terms of MAE accuracy. Applicability of the ANN was also analyzed with limitations of the chemical composition of ANNs and present reinforcement measures in the future studies through qualitative analysis comparisons based on Fourier transform infrared spectroscopy. Consequently, the relative effect was derived from the ANN hidden layer weight for specific calculation of the relationship between elemental composition and proximate analysis properties. As a result, it was possible to qualitatively analyze how the proximate analysis value affects the composition of elements and calculate the ratio accordingly. The findings of this study provide an improved and efficient approach to predicting the elemental composition of thermal coal, based on data from South Korean power plants. Also, further research can follow schematics from this study with the applicability and accessibility of the ANN.

Comparative Study of the Synergistic Effects of Blending Raw/Torrefied Biomass and Vietnamese Anthracite Using Co-pyrolysis.

Biomass can be upgraded via torrefaction, and torrefied kenaf (TK) is a fuel that allows blending with coal at high ratios. In the present study, raw kenaf (Hibiscus cannabinus L.) (RK) was torrefied at 523 K for 30 min and then mixed with Vietnamese anthracite (NinhBinh, NB) before co-pyrolysis. Thermogravimetric (TG) analysis was used to evaluate the behavior of RK, TK, and blended RK/TK during co-pyrolysis at biomass blending ratios (BBRs) of 0, 25, 50, 75, and 100 wt %. The TG and derivative thermogravimetry curves of a mixture of NB and RK (NBRK) were similar to those of RK. The decomposition curves of a mixture of NB and TK (NBTK) depended on the mass fraction of TK. Based on weight loss differences between the experimental and calculated data for the fuel blends, no interaction between the RK and anthracite was observed for all BBRs, whereas anthracite involving 50 and 75% TK exhibited synergistic effects. The temperature range for synergy and degree of synergy for NB and TK depended on the heating rate and mass ratio of TK. Kinetic parameters were calculated using the Friedman-non-isothermal free kinetic method at heating rates of 10, 20, and 40 K/min. The results showed that the activation energy (E) values of the NBRK at conversion ratios of 0.2-0.5 were equal to those of the RK, whereas they were superior at NB decomposition ratios of 0.6-0.8. NBTK1-1 (BBR of 50%) showed E values higher than those of NB at some conversion ratios, thus demonstrating a negative impact of blending. Further, NBTK1-3 (BBR of 75%) and NBTK3-1 (BBR of 25%) exhibited E values between those of NB and TK. The present study suggests that a high TK mass fraction (75%) in the blend for co-pyrolysis is optimal for the activation energy and volatile matter yield.

Methane Gas Cofiring Effects on Combustion and NO x Emission in 550 MW Tangentially Fired Pulverized-Coal Boiler.

A shift from coal to liquefied natural gas for electricity generation can mitigate CO2 emissions and respond to the intermittent and variable characteristics of renewable energy. With this objective, numerical simulation was performed in this study to determine the optimal position of the methane injector and evaluate the achievable reduction in NO x emissions before applying methane cofiring to an existing 550 MW tangentially fired pulverized-coal boiler (Boryeong Unit 3). The combustion and NO x reduction in the furnace were intensively analyzed based on the methane cofiring rate (up to 40%). The optimal position of the methane injector was found to be inside the oil port based on the spatial distribution of NO x and the stoichiometric ratio along the furnace height. The NO x reduction rate was logarithmically proportional to the methane cofiring rate, and compared to the base case, a 69.8% reduction was achieved at the 40% cofiring rate. In addition, the fraction of unburned char at the boiler outlet was equivalent to that of the existing boiler as the increase in the flow rates of the close-coupled and separated overfire air improved fuel and air mixing. Simultaneously, methane cofiring led to a reduction in the total fuel loss and CO emissions. Finally, this study showed that the recommended optimum cofiring rate was 20% based on the furnace exit gas temperature. Under the 20% methane cofiring condition, the boiler achieved a 57.3% reduction in NO x emissions and a 7.4% improvement in fuel loss.

Effect of Torrefaction on Thermal and Kinetic Behavior of Kenaf during Its Pyrolysis and CO2 Gasification.

Torrefaction is an appealing pretreatment method for improving the fuel properties of kenaf biomass before its utilization in thermochemical processes. This study evaluated and compared the impact of torrefaction on thermal behavior and kinetics during pyrolysis and gasification. Thermogravimetric analysis experiments were conducted at temperatures of 300-1173 K at several heating rates under N2 and CO2 atmospheres. The raw and torrefied kenaf (RK and TK) during CO2 gasification in the low-temperature region (<900 K) was found to exhibit a tendency that was similar to that of N2. However, TK during CO2 gasification resulted in a lower maximum mass loss rate, delayed initiating temperature, and lower devolatilization index due to lower reactivity. In addition, the gasification reaction of CO2 and char was observed to occur in the high-temperature region (> 900 K), thus improving conversion efficiencies. The activation energy for TK in a CO2 atmosphere depending on the conversion was calculated using the distributed activation energy method. The activation of RK during CO2 gasification was higher than that of N2. However, TK during CO2 gasification exhibited a lower activation energy compared to that of N2, indicating its potential as a better feedstock during the CO2 gasification process and the ability to save energy.

Copula-based algorithm for generating bursty time series.

Dynamical processes in various natural and social phenomena have been described by a series of events or event sequences showing non-Poissonian, bursty temporal patterns. Temporal correlations in such bursty time series can be understood not only by heterogeneous interevent times (IETs) but also by correlations between IETs. Modeling and simulating various dynamical processes requires us to generate event sequences with a heavy-tailed IET distribution and memory effects between IETs. For this, we propose a Farlie-Gumbel-Morgenstern copula-based algorithm for generating event sequences with correlated IETs when the IET distribution and the memory coefficient between two consecutive IETs are given. We successfully apply our algorithm to the cases with heavy-tailed IET distributions. We also compare our algorithm to the existing shuffling method to find that our algorithm outperforms the shuffling method for some cases. Our copula-based algorithm is expected to be used for more realistic modeling of various dynamical processes.

Hierarchical burst model for complex bursty dynamics.

Temporal inhomogeneities observed in various natural and social phenomena have often been characterized in terms of scaling behaviors in the autocorrelation function with a decaying exponent γ, the interevent time distribution with a power-law exponent α, and the burst size distributions. Here the interevent time is defined as a time interval between two consecutive events in the event sequence, and the burst size denotes the number of events in a bursty train detected for a given time window. To understand such temporal scaling behaviors implying a hierarchical temporal structure, we devise a hierarchical burst model by assuming that each observed event might be a consequence of the multilevel causal or decision-making process. By studying our model analytically and numerically, we confirm the scaling relation α+γ=2, established for the uncorrelated interevent times, despite of the existence of correlations between interevent times. Such correlations between interevent times are supported by the stretched exponential burst size distributions, for which we provide an analytic argument. In addition, by imposing conditions for the ordering of events, we observe an additional feature of log-periodic behavior in the autocorrelation function. Our modeling approach for the hierarchical temporal structure can help us better understand the underlying mechanisms behind complex bursty dynamics showing temporal scaling behaviors.

Two-in-one fuel combining sugar cane with low rank coal and its CO2 reduction effects in pulverized-coal power plants.

Coal-fired power plants are facing to two major independent problems, namely, the burden to reduce CO(2) emission to comply with renewable portfolio standard (RPS) and cap-and-trade system, and the need to use low-rank coal due to the instability of high-rank coal supply. To address such unresolved issues, integrated gasification combined cycle (IGCC) with carbon capture and storage (CCS) has been suggested, and low rank coal has been upgraded by high-pressure and high-temperature processes. However, IGCC incurs huge construction costs, and the coal upgrading processes require fossil-fuel-derived additives and harsh operation condition. Here, we first show a hybrid coal that can solve these two problems simultaneously while using existing power plants. Hybrid coal is defined as a two-in-one fuel combining low rank coal with a sugar cane-derived bioliquid, such as molasses and sugar cane juice, by bioliquid diffusion into coal intrapores and precarbonization of the bioliquid. Unlike the simple blend of biomass and coal showing dual combustion behavior, hybrid coal provided a single coal combustion pattern. If hybrid coal (biomass/coal ratio = 28 wt %) is used as a fuel for 500 MW power generation, the net CO(2) emission is 21.2-33.1% and 12.5-25.7% lower than those for low rank coal and designed coal, and the required coal supply can be reduced by 33% compared with low rank coal. Considering high oil prices and time required before a stable renewable energy supply can be established, hybrid coal could be recognized as an innovative low-carbon-emission energy technology that can bridge the gulf between fossil fuels and renewable energy, because various water-soluble biomass could be used as an additive for hybrid coal through proper modification of preparation conditions.

Application of the self-organizing map (SOM) to assess the heavy metal removal performance in experimental constructed wetlands.

The self-organizing map (SOM) model was applied to elucidate heavy metal removal mechanisms and to predict heavy metal concentrations in experimental constructed wetlands treating urban runoff. A newly developed SOM map showed that nickel in constructed wetland filters is likely to leach under high conductivity in combination with low pH in winter. In contrast, influent pH and conductivity were not shown to have clear relationships with copper (Cu) concentrations in the effluent, suggesting that the mobility of Cu was not considerably affected by salt increase during winter. The accuracy of prediction with SOM was highly satisfactory, suggesting heavy metals can be efficiently estimated by applying the SOM model with input variables such as conductivity, pH, temperature and redox potential, which can be monitored in real time. Moreover, domain understanding was not required to implement the SOM model for prediction of heavy metal removal efficiencies.