Smoke Concentration Research Articles

Efektivitas Asap Cair Kayu Rambutan dalam Menghambat Pertumbuhan Colletotrichum sp Secara In Vitro

Leaf blight is one of the main diseases in oil palm nurseries, as a result of the attack of the pathogen Colletotrichum sp. The initial symptoms are found on the edges of the leaves. Colletotrichum sp. is a pathogenic fungus that causes blight in oil palm, so it needs to be controlled. One of the alternative controls is by using rambutan wood liquid smoke. This study aims to obtain an effective concentration of rambutan wood liquid smoke that can inhibit the growth of Colletotrichum sp. in vitro. This research was carried out from November 2023 to January 2024 at the Laboratory of Pathology, Entomology, Microbiology and Soil Science, Faculty of Agriculture and Animal Husbandry, Sultan Syarif Kasim State Islamic University, Riau. This study used an experimental method with a complete randomized design (RAL) with 6 treatments (0%, 1%, 2%, 3%, 4% and 5%) with each treatment repeated 4 times, so that there were 24 experimental units. Observation parameters include total phenol analysis and PH test of liquid smoke, macroscopic, growth rate (cm/day) and inhibition (%) Colletotrichum sp. The results showed a total phenol content of 3.47% and a PH test. Rambutan wood liquid smoke with a concentration of 4% is very effective in inhibiting Colletotrichum sp. 100% inhibition and growth rate of 0 cm/day.

Effect of ambient pressure on the fire characteristics of lithium-ion battery energy storage container

As lithium-ion battery energy storage gains popularity and application at high altitudes, the evolution of fire risk in storage containers remains uncertain. In this study, numerical simulation is employed to investigate the fire characteristics of lithium-ion battery storage container under varying ambient pressures. The findings reveal that the peak heat release rate of fires at normal pressure is significantly higher than at lower pressure. Specifically, the heat release rate at 100 kPa is 9215 kW, exceeding the value at 40 kPa by 42%, which is only 3900 kW. This peak heat release rate also demonstrates a power function relationship with ambient pressure. In addition, fires tend to last longer in lower pressure, where high-temperature areas expand and spread rates increase. Moreover, higher pressures produce elevated peak concentrations of CO and CO2, while smoke spreads faster in lower pressure, despite lower peak smoke concentrations. The study findings can serve as a foundation for assessing the fire hazards and designing fire protection measures for lithium-ion battery storage containers exposed to varying ambient pressures.

A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Effectiveness of natural ventilation in controlling the smoke spread within a refuge floor of a high-rise building

Refuge floors in high-rise buildings ensure a haven for occupants to stay in an emergencyprimarily during a fire breakout. Provisions for natural ventilation of the refuge floor are mandatory in all fire codes as they help release smoke and heat build-up. However, the effectiveness of such provisions has been questioned in many studies. This research employs large eddy simulations based three-dimensional computational fluid dynamics modelling on a real-world high-rise building to investigate the efficacy of natural ventilation in a refuge floor during a fire. Consequently, the performance was assessed by evaluating the temporal and spatial distributions of temperature, visibility, smoke and carbon monoxide concentration. Emphasis was placed on determining the adequacy of window placement and openings. Analysis was started with a code-compliant window configuration. Subsequently the window size and the layout were iteratively modified using simulation results. Seasonal wind variations were also considered. Results showed that a well-engineered natural ventilation strategy can considerably improve the refuge floor habitability. Simulations also revealed limitations of natural ventilation under certain wind conditions, necessitating a backup mechanical system. This also underlined that combining fire analysis with cutting-edge modelling allows tailored natural ventilation for refuge floors, maximising safe occupancy during fires.

Artificial neural network based forecasting of diesel engine performance and emissions utilizing waste cooking biodiesel

Ecological and environmental problems resulting from fossil fuels are due to the harmful emissions released into the atmosphere. The rising interest in searching for alternative fuels like biodiesel is growing to solve these problems. Waste cooking oil (WCO) is transformed into methyl ester and combined with biodiesel in percentages of 25, 50, 75, and 100%. Research is done on the impacts of methyl ester blends on engine performance and emissions. Compared to diesel, the methyl ester combination showed 25% lower brake power and 24% loss in thermal efficiency at maximum load and 1500 rpm. However, diesel fuel showed 23% lower specific fuel consumption increase than biodiesel. Compared to diesel, methyl ester exhibits 15% lower air-fuel ratio and 4% volumetric efficiency. Biodiesel lowers CO, HC, and smoke concentrations by 12, 44, and 48%, respectively, compared to diesel. Biodiesel emits 23% higher NOx at 1500 rpm and 100% engine load. To predict the emissions and performance of different percentages of biodiesel at engine speed variation, an artificial neural network (ANN) model is presented. ANN modeling minimizes labor, time, and finances and uses nonlinear data. Predictions were produced about the brake output power, specific fuel consumption, thermal efficiency, air-fuel ratio, volumetric efficiency, and emissions of smoke, CO, HC, and NOx as a function of engine speed and blend ratio. All correlation coefficients (r) over 0.99 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} values were beyond 0.98 for all variables. There were low values of MSE, MAPE, and MSLE with significant predictive ability. WCO’s biodiesel is a viable diesel engine replacement fuel.

A flexible perception method of thin smoke based on patch total bounded variation for buildings.

Early fire warning is critical to the safety and stability of power systems. However, current methods encounter challenges in capturing subtle features, limiting their effectiveness in providing timely alerts for potential fire hazards. To overcome this drawback, a novel detection algorithm for thin smoke was proposed to enhance early fire detection capabilities. The core is that the Patch-TBV feature was proposed first, and the total bounded variation (TBV) was computed at the patch level. This approach is rooted in the understanding that traditional methods struggle to detect minute variations in image characteristics, particularly in scenarios where the features are dispersed or subtle. By computing TBV at a more localized level, the algorithm proposed gains a finer granularity in assessing image quality, enabling it to capture subtle variations that might indicate the presence of smoke or early signs of a fire. Another key aspect that sets our algorithm apart is the incorporation of subtle variation magnification. This technique serves to magnify subtle features within the image, leveraging the computed TBV values. This magnification strategy is pivotal for improving the algorithm's precision in detecting subtle variations, especially in environments where smoke concentrations may be minimal or dispersed. To evaluate the algorithm's performance in real-world scenarios, a comprehensive dataset, named TIP, comprising 3,120 images was constructed. The dataset covers diverse conditions and potential challenges that might be encountered in practical applications. Experimental results confirm the robustness and effectiveness of the proposed algorithm, showcasing its ability to provide accurate and timely fire warnings in various contexts. In conclusion, our research not only identifies the limitations of existing methods in capturing subtle features for early fire detection but also proposes a sophisticated algorithm, integrating Patch-TBV and micro-variation amplification, to address these challenges. The algorithm's effectiveness and robustness are substantiated through extensive testing, demonstrating its potential as a valuable tool for enhancing fire safety in power systems and similar environments.

Potential of Liquid Smoke from Corn Cobs as an Antioxidant Source in Mackerel Preservation

This study aims to determine the antioxidant potential contained in liquid smoke using the DPPH method, as well as the effects of the concentration and soaking duration of liquid smoke from corn cobs on phenol content, moisture content, and organoleptic testing. The study first tested the antioxidant activity of liquid smoke from corn cobs by measuring the 50% Inhibition Concentration (IC50) value. Subsequently, liquid smoke was used at various concentrations (0%, 5%, 7%, and 10%) with a 60-minute soaking time, and a 7-day storage period was followed by testing for phenol content, moisture content, and organoleptic properties. Data analysis was performed using SPSS.26 with ANOVA testing. The results showed an IC50 value of 11.642 ppm in the antioxidant test of liquid smoke, indicating its effectiveness in neutralizing free radicals. The addition of liquid smoke to mackerel with different concentration variations affected phenol content, moisture content, and organoleptic properties. The optimal concentration of liquid smoke for use as a fish preservative is 10%

Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

A study of quantifying the influence of kitchen human activity on indoor air quality dynamics

Indoor air quality (IAQ) is increasingly recognised as one of the critical factors influencing human health, particularly given the amount of time people spend indoors. This study investigated the impact of real-life kitchen human activity (KHA) on IAQ. We used low-cost sensors to measure real-time concentrations of smoke, carbon monoxide (CO), and particulate matter (PM10 and PM2.5) in the kitchen of a household with three adults, analysing KHAs by dividing them into five categories. The fixed effect model was employed to analyse the data, explaining the impact of different KHAs on IAQ. The results showed that compared to other KHAs, using the gas stove had the greatest impact on IAQ, with average increases of 13% in smoke, 24.4% in CO, 9.8% in PM10, and 5.34% in PM2.5. The study also found that without windows and with insufficient ventilation, only using the range hood cannot effectively and obviously reduce PM levels. These findings highlight the need for comprehensive IAQ management strategies and further research. Despite its limitations, this study also validated the potential of low-cost sensors in IAQ monitoring.

Adaptive wildfire spread prediction for complex terrain: modeling the effectiveness of sprinkler systems

BackgroundBecause the threat of wildfires to global ecosystems and society continues to rise, this study provides an experimental simulation framework that assesses the spread and reduction of wildfires to evaluate the effectiveness of adaptation methods in reducing their impact. The process entails selecting a vulnerable wildfire area and adaptation method, then generating the computational fluid dynamics (CFD) model. Monitoring data are then used to configure the model, set boundary conditions, and simulate the fire. The effectiveness of the adaptation method in minimizing damage in the area of interest is evaluated by comparing simulations with and without the chosen adaptation method. Our focus area was a natural recreational forest in Wonju, Gangwon-do, Korea, and our adaptation method was a water sprinkler system.ResultsOur framework provides aims to provide an experimental means of assessing the wildfire spread path and spread area based on exogenous variables of wind speed, wind direction, relative humidity, and more. The sprinkler adaptation had a reduction effect of 20% in the wildfire spread rate for the 10-h period, which refers to the time limit of the simulation after ignition. We revealed that at higher wind speeds, the fire primarily follows the wind direction; whereas at lower wind speeds, the fire is more influenced by the topography. Additionally, 60 min after ignition, the adaptation methods can suppress wildfire spread by > 70%. Notably, sprinklers reduce smoke concentrations by up to 50% (ppm) over the affected area.ConclusionsThis study demonstrates the potential effectiveness of a comprehensive CFD model in mitigating wildfire spread using sprinkler systems as an experimental analysis. Key results include a 20% reduction in wildfire within 10 h of ignition, significant influence of wind speed on spread patterns, and a reduction of smoke concentrations, improving air quality. These findings highlight the potential of CFD-based frameworks to enhance wildfire response strategies. However, it is important to note that this study’s limitations include the lack of experimental or measured fire behavior data, which should be considered when interpreting the effectiveness of the CFD model.

The Effect of Expanded Vermiculite on the Fire Resistance of Waterborne Acrylic Coatings.

Due to their ability to prevent or slow the spread of fires, fire-retardant coatings are utilized as the main means of fire protection for steel structures, combining easy application and high economic efficiency. This study investigates the effects of the particle size and dosage of expanded vermiculite (EV) on the fire resistance and application performance of coatings. Ammonium polyphosphate, melamine, and pentaerythritol were used as intumescent fire-retardant systems, along with waterborne hydroxyl-modified acrylic resins as the film-forming substances. The properties of fire resistance coatings were tested via scanning electron microscope (SEM), X-ray diffractometry (XRD), thermogravimetric analysis (TGA), limiting oxygen index (LOI), and cone calorimetry. The excellent fire performance of the coatings with 3 wt.% 300-mesh EV was proven, exhibiting a relative expansion of 30.43 times. Moreover, the surface structure of the charcoal layer was dense. The total smoke production (TSP) and smoke concentration (TSR) were only 0.18 m2 and 0.25 m2/m2.

Investigation into the effects of background particles on Serratia marcescens bioaerosol

Bioaerosols play a significant role in the transmission of infectious diseases in enclosed spaces. Previous studies have shown that certain background particles can influence bioaerosol concentrations. However, the characteristics of the quantitative effects of these particles are unclear. This study investigates the influence of various background particles and their concentrations on bioaerosol concentrations through decay experiments, complemented by antibacterial assessments of different particle materials. The results showed that the decay rate of bioaerosols increased with an increase in mosquito-repellent smoke concentration. Moreover, the size of bioaerosols increased over time. Different background particles demonstrated varying effects on bioaerosols. The presence of DEHS (Di-Ethyl-Hexyl-Sebacate), NaCl, and NB (nutrient broth) background particles did not significantly alter the decay trends of bioaerosol. DEHS and NaCl did not exert any observable antibacterial effects, whereas mosquito-repellent smoke demonstrated notable bacteriostasis. These findings suggest that the enhanced decay of bioaerosols is likely due to their coagulation and deactivation in the presence of background particles. This study offers insights into the quantitative dynamics influenced by background particles, thereby enriching our understanding of bioaerosol behavior in indoor environments.

Effectiveness of Tea Leaf (Camellia sinensis) Liquid Smoke as an Antiseptic

Microbes are often the cause of infectious diseases; one method of prevention is to use antiseptics. Excessive use of antisep- tics can cause mild to severe side effects, so an alternative antiseptic is needed. Liquid smoke is an alternative raw material to produce antiseptics because its main component can be used as an inhibitor of microbial growth. This study aims to determine the effectiveness of tea-leaf liquid smoke as an antiseptic against microbial growth in vivo and in vitro. The method for producing liquid smoke via pyrolysis involves distilling grade 3 liquid smoke to generate grade 1 and grade 2 liquid smoke. The in vitro test uses a microbial growth inhibition test with grade 1 and 2 liquid smoke concentrations of 35%, 50%, and 75%. The microbes used were Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, Candida albicans ATCC 10231, and Aspergillus flavus ATCC 9643. The in vivo test uses the swab test method on the palm of the hand. The results of the invitro test showed that the largest inhibition zone of tea leaf liquid smoke was obtained using grade 2 liquid smoke with a concentration of 75%.The results of the in vivo test showed that the effectiveness of tea leaf liquid smoke was 80% against bacteria and 85% against fungi. Based onthe results of the study, it can be concluded that tea leaf liquid smoke (Camellia sinensis) has potential as an antiseptic.

The Use of Liquid Rubber Smoke as PGR on Coffee Seeds Germination

The objectives of this research is to obtain the best concentration of liquid rubber smoke as a growth regulator in the germination of Arabica, Robusta and Liberica coffee seeds. Method of research: location of the research was on Seed Technology Laboratory University of Jambi, the research design was carried out using a randomized block design consisting of a factorial pattern based on 3 factors (Types of Coffee species: Arabica from Kerinci, Robusta from Merangin, and Liberica from Tanjung Jabung Barat), 3 levels of treatment for providing liquid smoke rubber 0%, 2.5%, 5%, by soaking for 5 minutes. With 3 repetitions test. Statistical data analysis by anova using SPSS. The result shows that 3 species of coffee give different response of the treatments, Arabica and Robusta seeds species has relative equal statistical response to Liberica coffee, concentration 2.5% of diluted liquid rubber smoke give the best germination and vigor seeds response performance.

Thyroid function monitoring during pregnancy in euthyroid women with thyroid autoimmunity.

Thyroid autoimmunity (TAI) may be present in 1-17% of pregnant women. Monitoring of thyroid function in euthyroid pregnant women positive for anti-thyroperoxidase antibodies (TPOAb+) is recommended. To determine the prevalence and possible clinical and biological risk factors of biochemical progression (rise in serum thyroid-stimulating hormone (TSH) > 2.5 mU/L) at second blood sampling during pregnancy, in euthyroid women (TSH ≤ 2.5 mU/L) according to their TPOAb status. This study included demographic and biological data from two previously published cohorts (n = 274 women from August 1996 to May 1997 Copenhagen cohort, and n = 66 women from January 2013 to December 2014 Brussels cohort) having at least two measurements of TSH and free thyroxine (FT4) and at least one of TPOAb during spontaneously achieved singleton pregnancies. The majority of women studied did not show biochemical progression. Only 4.2% progressed, significantly more frequently among TPOAb+ women, as compared to TPOAb- group (9.4 vs 2.7%, P = 0.015). No rise in serum TSH > 4 mU/L at 2nd sampling was observed. Higher baseline TSH levels were associated with biochemical progression in both TPOAb+ (P = 0.05) and TPOAb- women (P < 0.001), whereas maternal age, BMI, multiparity, smoking, FT4, and TPOAb concentrations were not significantly different between women with and without progression. Only a minority of euthyroid women with thyroid autoimmunity presented biochemical progression and none with a TSH > 4 mU/L. Larger studies are needed to better target the subset of women who would benefit most from repeated thyroid function monitoring during pregnancy.

Determination of Fatty Acid (Epa, Dha, Cholesterol) and Phenols in Squid (Loligo Sp.) with Addition Different Liquid Smoke Concentrations

Determination of Fatty Acid (Epa, Dha, Cholesterol) and Phenols in Squid (Loligo Sp.) with Addition Different Liquid Smoke Concentrations

Multi-Height and Heterogeneous Sensor Fusion Discriminant with LSTM for Weak Fire Signal Detection in Large Spaces with High Ceilings

Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are weakened, leading to delays in fire detection by sensor networks. This paper proposes a multi-height and heterogeneous fusion discriminant model with a multilayered LSTM structure for the robust detection of weak fire signals in such challenging situations. The model employs three LSTM structures with cross inputs in the first layer and an input-weighted LSTM structure in the second layer to capture the temporal and cross-correlation features of smoke concentration, temperature, and plume velocity sensor data. The third LSTM layer further aggregates these features to extract the spatial correlation patterns among different heights. The experimental results demonstrate that the proposed algorithm can effectively expedite alarm response during sparse smoke conditions and mitigate false alarms caused by weak signals.

In vitro potential control of white root fungus in nutmeg (Myristica fragrans) based on botanical fungicide of coconut shell liquid smoke

Nutmeg (Myristica fragrans) is spice plant which has an important position as a source of essential oils. An important problem in nutmeg cultivation is white root disease which can reduce nutmeg production. Until now, white root disease is still a problem in nutmeg plantations in South Aceh district. One of the solution that can be explored as an environmentally friendly control measure is to use the botanical fungicide coconut shell liquid smoke. This research aims to determine the potential of coconut shell liquid smoke in controlling white root fungus in vitro. The research was carried out in the Plant Protection Laboratory, Faculty of Agriculture, Teuku Umar University. The research design used was a non-factorial Completely Randomized Design (CRD) consisting of nine levels of liquid smoke concentration treatment; control 0%, 0.5%, 1%, 2%, 3%, 4%, 6%, 8%, 10%. Each treatment involved three replications. Data analysis was carried out using analysis of variance and Tukey's least significant difference (HSD) test.The research stages carried out consisted of producing coconut shell liquid smoke, isolating white root fungus from the roots of infected nutmeg plants and potential assay of liquid smoke in inhibiting the growth of white root fungus. In vitro bioassay result showed that liquid smoke treatment 3% was the best treatment because it was the lowest concentration causing total inhibition of the growth of white root fungus.

Revealing patterns in improving the performance of portable smoke and heat removal devices when using fine sprayed water

An issue related to the application of portable smoke and heat removal devices for buildings and structures is to provide for their effectiveness when removing smoke and reducing temperature. That us why the object of this study was a change in the performance of a portable smoke and heat removal device when using finely sprayed water. Improved efficiency of the use of portable smoke and heat removal devices was based on experimental studies. It was proved that the reduction of smoke concentration and temperature in the room to the initial conditions was achieved for independent smoke dispersion in 5340 s, and cooling occurred in 3120 s. With the use of a smoke and heat removal agent during air supply, the smoke dissipated in 720 s, and cooling took 1560 s. And with the simultaneous supply of air and a sprayed jet of water, smoke dispersal occurred in 360 s, and cooling in 1020 s, respectively. To determine the efficiency of the smoke and heat release device, a calculation-experimental method has been devised that makes it possible to estimate the coefficient of the smoke and heat release agent when supplying air and water. According to experimental data, it was calculated that the coefficient of effectiveness of the smoke and heat removal devices when supplying air and water compared to the means when supplying only air increases by 2.1 times. The practical significance is that the results were taken into account when devising methodical recommendations for extinguishing fires. Thus, there are reasons to assert the possibility of targeted regulation of the processes of smoke reduction and temperature reduction through the use of a smoke and heat release device that simultaneously supplies air and finely sprayed water

CFD Simulation to Assess the Effects of Asphalt Pavement Combustion on User Safety in the Event of a Fire in Road Tunnels

This paper presents a specific 3D computational fluid dynamics model to quantify the effects of the combustion of asphalt road pavement on user safety in the event of a fire in a bi-directional road tunnel. Since the consequences on tunnel users and/or rescue teams might be affected not only by the tunnel geometry but also by the type of ventilation and traffic flow, the environmental conditions caused by the fire in the tunnel under natural or longitudinal mechanical ventilation, as well as congested traffic conditions, were more especially investigated. The simulation results showed that the combustion of the asphalt pavement in the event of a 100 MW fire, compared to the case of a non-combustible road pavement, caused (i) an increase in smoke concentrations; (ii) a greater number of users exposed to the risk of incapacity to escape from the tunnel; (iii) a more difficult situation for the firefighters entering the tunnel upstream of the fire source in the case of natural ventilation; (iv) a higher probability of the domino effect for vehicles queued downstream of the fire when the tunnel is mechanically ventilated.