Mixture Proportions Research Articles

Trade-offs and management strategies for ecosystem services in mixed Scots pine and Maritime pine forests

AbstractMixed forests are increasingly recognized for their resilience to climate change and enhanced ecosystem services (ESs) provision, making them a focal point for sustainable forest management strategies. This study examines the trade-offs in ESs provision between pure and different proportions of mixed stands of Scots pine (Pinus sylvestris L.) and Maritime pine (Pinus pinaster Ait.) in the Northern Iberian Range, Spain. Using the SIMANFOR simulation platform, we evaluated various silvicultural scenarios developed to obtain different ESs such as carbon sequestration, timber and mushroom yields. Our findings reveal that ESs provision varies depending on the forest type (pure or mixed) and the mixture proportion, following different trends on each ES. The initial species proportions and their maintenance were less critical than the management approach itself, which significantly influenced ESs outcomes. Focusing solely on individual ESs can lead to trade-offs, as highlighted by our study on silviculture focused on large saw timber yields. However, adopting a balanced approach that considers multiple ESs can mitigate these trade-offs. Our findings underscore the effectiveness of this approach in maximizing yields of mushrooms, sequestered carbon, and small saw timber. This research provides valuable insights for forest managers aiming to balance productivity and sustainability in ESs provision, providing strategies to maximize compatible ESs effectively.

Comprehensive analysis of formaldehyde and acetaldehyde emissions and performance of a diesel engine using various diesel-vegetable oil and diesel-biodiesel blends

This study investigates the emissions of formaldehyde and acetaldehyde, as well as the performance of a diesel engine fueled by various diesel-triglyceride and diesel-biodiesel blends. The biodiesels were synthesized from triglyceride sources, including canola, soybean, palm, coconut oil, and beef tallow. Emission analyses were conducted using Differential Optical Absorption Spectroscopy (DOAS). The biodiesel was blended with diesel in specific proportions, allowing for a comparative analysis of formaldehyde and acetaldehyde emissions. Results indicate that the addition of triglycerides and biodiesel to diesel fuel leads to increased emissions of both formaldehyde and acetaldehyde in the engine exhaust, with higher emission levels observed in mixtures containing triglycerides. Notably, canola oil blended with diesel exhibited lower formaldehyde and acetaldehyde emission values than other diesel-vegetable oil blends. Similarly, palm biodiesel demonstrated reduced emission levels compared to other diesel-biodiesel blends. This study concludes that both triglycerides and esters can be effectively blended with diesel fuel for combustion in engines, benefiting from their oxygenating properties that enhance engine performance. However, the performance advantage is most pronounced with mixture proportions of up to 5% for triglycerides and 15% for biodiesel.

TPE-xgboost for explainable predictions of concrete compressive strength considering compositions, and mechanical and microstructure properties of testing samples

The use of machine learning (ML) for predicting concrete compressive strength (CCS) has shown promising and accurate results, making it a valuable tool in the field. However, efficient prediction requires not only a robust ML approach but also a comprehensive and well-curated dataset. This study addresses this challenge by investigating an extensive and integrated dataset of (1) concrete compositions (mixture proportions) and (2) testing conditions (mechanical and microstructure properties of concrete testing samples), containing 1525 observations relating to 39 parameters. On algorithms side, this research proposes novel tree-structured parzen estimator based extreme gradient boosting (TPE-xgboost) for accurate and confident CCS predictions. Moreover, SHapley Additive exPlanations (SHAP) analysis was performed to provide a comprehensive understanding of feature importance, dependencies, and interactions. Compared to prior research, this study demonstrates significant improvements in CCS prediction accuracy for separate investigations on concrete compositions (3.77%) and mechanical and microstructure properties (28.57%), while maintaining reasonable accuracy for the integrated dataset despite its complexity and sparseness. SHAP analysis reveals key influential factors, including age and water-to-binder ratio in concrete composition, and peak load and height of testing samples, as well as microstructural characteristics such as mean values of global autocorrelation length and its integral range. This research provides valuable insights into model optimization, predictive performance, and feature dependencies and interactions, contributing to the development of more accurate and reliable CCS prediction models.

Class-prior probability estimation using density ratio between unlabeled instances and positively labeled noisy instances

In this paper, the authors propose a new approach for determining the mixture proportions of positive and negative instances in both a small set of positively labeled instances contaminated with an unknown proportion of negative instances and an unlabeled instance set with an unknown mixture of positive and negative instances. The unique advantage of this method lies in its ability to derive these proportions with a high level of accuracy by directly leveraging probability density ratio estimation. Experiments conducted on synthetic and UCI repository data demonstrate that the proposed technique can robustly estimate class-prior probabilities with greater speed and better accuracy compared to existing methods, particularly in the presence of instance number and imbalance challenges.

Co-firing characteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO2 (S-CO2) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO2 power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO2 CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results, a novel Improved Whale Optimization Algorithm and Bi-dictionary Long Short-Term Memory (IWOA-BiLSTM) algorithm model is established to predict CFB temperature, NOx emission concentration, and SO2 emission concentration, respectively. Results show that CO and SO2 decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx declines, and the change of SO2 is not obvious. Compared with two other basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.032 %, 0.231 %, and 0.157 %, respectively, which can meet the prediction requirements with acceptable accuracy.

Rapid and supersensitive allele detection of Plasmodium falciparum chloroquine resistance via a Pyrococcus furiosus argonaute-triggered dual-signal biosensing platform

BackgroundMalaria remains a serious public health problem worldwide, particularly in Africa. Resistance to antimalarial drugs is an essential issue for malaria control and elimination. Currently, polymerase chain reaction (PCR) combined with Sanger sequencing is regarded as the gold standard for mutation detection. However, this method fails to meet the requirements of point-of-care testing (POCT) because of its time-consuming, expensive instruments and professional dependence. To support this strategy, we developed a novel diagnostic platform that combines recombinase polymerase amplification (RPA) with the Pyrococcus furiosus argonaute (PfAgo) protein and was designed to detect gene mutations related to antimalarial drug resistance. The Pfcrt haplotypes CVMNK and CVIET of chloroquine resistance (CQR) were used as examples and were assessed in this study.MethodsBy meticulously designing strategies, RPA primers, guide DNAs, and probes were screened, the reaction was optimized, and the resulting parameters were employed to ascertain the genotype of Pfcrt. The recombinant plasmids pUC57/Pfcrt-CVIET and pUC57/Pfcrt-CVMNK were constructed and diluted for sensitivity detection. The pUC57/Pfcrt-CVIET plasmid mixture was added to the pUC57/Pfcrt-CVMNK plasmid mixture in different additions to configure several specific proportions of mixed plasmid mixtures. The RPA-PfAgo platform was used, and the mixed plasmid was detected simultaneously via nest-PCR (nPCR) and Sanger sequencing. The platform was then evaluated on 85 clinical samples and compared with Sanger sequencing.ResultsThe entire process achieves the key mutation Pfcrt-CVMNK/CVIET genotype identification of CQR within 90 min. The platform achieved 1.8 × 104 copies/μL sensitivity and could detect as little as 3% CVIET in mixed plasmids, which is a higher sensitivity than that of Sanger sequencing (5%). Notably, the platform shows 100% concordance with the gold standard method when 85 clinical samples are tested. The sensitivity and specificity were 100% for the 85 clinical samples.ConclusionsThis study established an RPA-PfAgo platform for genotyping the key mutation Pfcrt-CVMNK/CVIET of CQR. This method can rapidly produce reliable results and avoid the disadvantages of nPCR with sequencing. This approach has the characteristics of a short operation time, low device dependence, and a good match to the POCT strategy, suggesting that the platform can be easily applied locally or on site.Graphical abstract

Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms.

Single sensors have been developed for specific gas detection in real-time environments, but their selectivity is limited by interference from other gases when considering mixtures of gases. Consequently, accurate detection of target gases in mixed gas environments is essential. Therefore, this study develops a sensor array approach to quantitatively estimate the concentration of carbon monoxide (CO) and nitrogen dioxide (NO2) gases in their binary mixture (CO and NO2). Thesensor array consists of two different sensors, developed with zinc oxide and graphene-cobalt sulfide. The sensor array wastested in the presence of 29 different proportions of the binary mixture at room temperature. Subsequently, machine learning (ML) algorithms are applied on sensor signals to estimate the concentration of gases. The ML models unfortunately exhibited inaccurate prediction when all sensor signals were considered, therefore, to improve the prediction accuracy, the sensor signals were divided into three levels based on the mixed gas concentration regime. Interestingly, the classification and regression algorithms provided good classification accuracy (85.13 ± 3.2%) and reasonable predictions of target gas concentrations at three levels. The proposed computational framework can be extended to include additional gases in mixed gases and used in various applications, including automotive, industrial, and environmental monitoring.

Mixture proportion optimization and durability evaluation of rejuvenating composite seal

Composite seal (CS) is effective in restoring pavement surface function and prolonging the service life, employing a double-layer structure composed of micro-surfacing and chip seal. Rejuvenating composite seal (RCS) applying rejuvenating agents to the chip seal is dedicated to revitalizing the aged asphalt of the original pavement surface. However, limited studies have focused on enhancing interlayer and temperature performance through optimizing mixture ratios. In this study, a modified emulsified asphalt with self-developed rejuvenating agent was applied, and the orthogonal test considering interlayer shear resistance and high temperature stability was conducted for initially mixture proportion of RCS, then the preliminary ratio was optimized. The proportion for micro-surfacing was determined by mixing test, wet track abrasion test, and loaded wheel sticking sand test. Simultaneously, the recommended asphalt spraying amount of rejuvenating chip seal was determined by utilizing accelerated loading abrasion test. In view of the abrasion resistance, interlayer adhesion, and cracking resistance, the coupled effects of aggregate size and paving rate of rejuvenating chip seal were studied. Long-term wet track abrasion test, freeze-thaw cycle test, and UV aging test were conducted to evaluate the durability. The results show that the asphalt-aggregate ratio of micro-surfacing is 7.1 %. For 4.75 mm or 7 mm rejuvenating chip seal, the recommended asphalt spraying amount is 1.5 kg/m2 or 1.6 kg/m2, and the range of paving rate is 60 %–70 %. For 9.5 mm or 13.2 mm rejuvenating chip seal, the results are 1.7 kg/m2 or 1.8 kg/m2, and 80 %–90 %, respectively. Under the conditions, RCS has better interlayer shear resistance and temperature performance, which can improve durability.

Investigation into the sheet metal forming process using solid granules medium with graphite powder addition

ABSTRACT The solid granules medium sheet metal forming process uses loose particles instead of a rigid convex mold in the deep drawing process. This study focused on a mixed medium of solid granules with added graphite powder , establishing numerical models for four different proportions of graphite powder-solid granules mixtures. A finite element simulation of deep drawing was conducted using 08AL sheets. A deep drawing test shows that the thickness distribution curve closely aligns with the numerical simulation. By adding graphite powder to the solid granules medium, the lubrication conditions between the granules and the sheet metal can be modified, increasing the thickness in the fracture-prone areas of the cylindrical part. Utilizing this numerical model yields thickness distribution and deformation characteristics that closely resemble those observed in deep drawing tests, thereby contributing significantly to the optimization and advancement of this forming technology.

Optimization of alkali-activated ladle slag-fly ash composites using a Taguchi-TOPSIS hybrid algorithm

The effect of multiple mix design factors on the properties of ladle slag-fly ash alkali-activated composites was investigated. Taguchi-TOPSIS hybrid algorithm was adopted to optimize mix design parameters, including ladle slag replacement by fly ash (LSR), sodium hydroxide molarity (SHM), the ratio of sodium silicate to sodium hydroxide (NS/NH), the ratio of alkaline activator solution to binder (AAS/B), and crushed stone replacement by desert dune sand (CSR). The results revealed that the mix proportions of the optimum strength response comprised LSR, AAS/B, SHM, NS/NH, and CSR of 10%, 0.5, 8 M, 2, and 75%, respectively, with a compressive strength of 21 MPa. Conversely, the mixture proportions for superior fresh properties had a flow of 240 mm and entailed LSR, AAS/B, SHM, NS/NH, and CSR of 40%, 0.5, 8 M, 2.5, and 75%, respectively. Additionally, the hybrid method prediction model proved to be robust, with the ability to predict strength and workability at 93 and 100% accuracy. The optimum mixes comprised an intermix of calcium aluminosilicate hydrate and sodium aluminosilicate hydrate gels, with traces of calcium silicate hydrate gel, as identified by microstructure analysis and using ternary diagram system of Ca/Si-Na/Si-Al/Si ratios.

Compare the Properties of Composite Material by Date Leaves with Different Additives

Almost no product is made today without the use of composite materials, which have demonstrated their value over time in a variety of applications. Examples include household products, automobiles, oil and gas stations and spacecraft. Instead of using timber, leaves will be used to create a solid material. The goal of this study is to identify the distinguishing features of composite materials made from dates palm waste, as well as the overall effects on the properties when changing the mixture's proportions and the amount to which those materials are vulnerable to outside influences. It also aims to identify the best material that can be produced and used in home furnishings, manufacturing, architectural design, practical applications, and other science-related fields. This project will address the use of natural fibers from dates palm waste and some other materials to convert them into a strong, lightweight, low-cost composite material to replace wood, aluminum, synthetic fibers, and other materials in industries and uses. It will also explore the manufacturing processes, testing methods, and the outcomes of comparing it with other materials. The UHU (adhesive) matrix has the lowest hardness value compared to others. Structure tests show composites' internal composition; it plays an important role in understanding the properties of composites. The water absorption test demonstrates the duration required for composites to become saturated with water, with optimal results falling within the range of 13 to 24 hours for saturation.

Design and microstructural analysis of the mixture proportion of alkali-activated fly ash-slag composite cementitious material

Abstract To explore the resource utilization of fly ash, slag, and coal gangue, the composition of hydration products and strength characteristics of fly ash-slag composite cementitious material (FSGF) were studied with NaOH as an alkali activator. First, response surface analysis was used to determine factors influencing the optimal NaOH content, basalt fiber dosage, and length to obtain the complete mix ratio of the composite cementitious material. Microscopic techniques such as XRD, FTIR, TG-DSC, and SEM were employed to analyze the crystal structure, thermal properties, and micro-morphology of the composite cementitious material, and to investigate the mechanism of NaOH-activated fly ash-slag cementitious material. The results indicated that the sensitivity of each factor affecting the mechanical properties of the composite cementitious material followed this sequence: NaOH content > basalt fiber length > basalt fiber dosage, with varying degrees of interaction among them. When the mass ratio of fly ash, slag, and coal gangue was 5:1:4, with 3% NaOH by weight, 2% basalt fiber dosage, and a fiber length of 3 mm, the optimal mix was achieved. The composite material achieved a compressive strength of 8.97 MPa after 28 days of standard curing at room temperature. NaOH, as an alkali activator, provided the strong alkaline environment required for the initial hydration of fly ash-slag composite cementitious materials, promoting the hydration of slag and fly ash. The hydration products in the fly ash-slag composite system were unevenly distributed, primarily consisting of gels like C-S-H, C-A-H, and C-A-S-H. NaOH was highly effective as an alkali activator in the fly ash-slag system.

Mechanical and Durability Properties of Rubberized Sulfur Concrete Using Waste Tire Crumb Rubber.

The use of rubber crumbs provides a viable solution for alleviating the disposal problem of waste tires. In this study, rubberized sulfur concrete (RSC) was researched to investigate the optimal mixture proportion and to improve the mixing process in terms of compressive strength and durability performance. For the mixture of the RSC, sand, rubber particles, and micro-filler were adopted as aggregates and sulfur was used for the binding material. Moreover, two mixing processes were applied: the dry mixing process and the wet mixing process. Based on the test results, the increment of rubber particles in the mixture led to a decrease in the compressive strength for both the dry and wet mixing processes. To minimize the voids between the sand and rubber particles, the micro-filler was used at 5% of the total volume. The amount of sulfur varied slightly depending on the mixing process: 30% sulfur for the dry mixing process and 34% sulfur for the wet mixing process, respectively. Consequently, compared to the dry mixing process, the wet mixing process increased the bonding force between sulfur and rubber powder due to the simultaneous heating and combining. In toughness, the wet mixing process demonstrates a 40% higher energy absorption capability compared to the dry mixing process. For the durability performance of the RSC, the mixture with 20% rubber particles produced using the wet mixing process exhibited better corrosion and freeze-thaw resistance.

Analisa Perbandingan Biaya dan Mutu Beton Menggunakan Serbuk Cangkang Telur untuk Mereduksi Pemakaian Semen

Concrete is a very important construction material in construction so that the use of concrete is widely used. In the use of conventional concrete, there are many problems that arise, such as the use of so much cement that can affect the environment. In this study, the use of eggshell powder will be used to reduce the amount of cement in the proportion of the concrete constituent mixture as well as in the use of powder and can reduce the cost of using cement which is relatively expensive. In addition, chicken egg shell waste contains calcium carbonate where it is known that one of the constituent ingredients of Portland Cement (SP) is calcium carbonate. This study uses SNI 03-2834-2000, to calculate the proportion of the concrete mixture. Testing the compressive strength of this concrete using cylindrical specimens with a diameter of 150 mm (0.15 m) and a height of 300 mm (0.30 m) as many as 15 samples with the amount of chicken egg shell powder used at 0%, 1%, 2%, 3% and 4%. From the results of testing the average compressive strength at the age of 7 days and then converted to the age of 28 days, the average compressive strength at 0% eggshell percentage is 25.76 MPa, 1% is 22.80 MPa, 2% is 20.43 MPA, 3% is 20.13 MPa, 4% is 19.98 MPa. The greater the percentage of chicken egg shell powder, the compressive strength of the concrete decreases. For the cost of 0% concrete mix is Rp. 11,414.5, 1% is Rp. 11,342.5, 2% is Rp. 11,270.5 3% is, Rp. 11,198.5, 4% is Rp. 11,150.5 the cost of concrete using egg shell powder relatively cheaper.

Mixture reliability analysis of a product consisting of new and remanufactured components

PurposeThe rapid increase in importance of the remanufacturing operation in the present scenario is just because of its ability to retrieve the functional value of the End-of-Use or End-of-Life products which is as good as the original product. However, customers are still concerned about the reliability of the remanufactured product which is considered as one of the major problems in the area of remanufacturing. The purpose of this paper is to study and analyse the behavioural pattern of the mixture failure rate of a remanufactured product.Design/methodology/approachIn order to analyse the behavioural pattern of the mixture failure rate, different proportions of new and remanufactured products are mixed. In this paper, a two-parameter Weibull distribution is used to observe the mixture failure rate characteristics. Also, the mixture failure rate of the remanufactured product is evaluated under two conditions, that is when the shape parameter of new and remanufactured components is the same and when the shape parameter values are different.FindingsFrom the analysis, it is observed that the mixture failure rate is always decreasing in nature when the shape parameter values are same. In that case, the value of the mixture failure rate depends only on the proportion of the new components. When the shape parameter values are different, the mixture failure rate characteristics depend upon the shape parameter value of the remanufactured product.Originality/valueThe results of the research can be applied to any remanufactured automotive product. This study also shows the behavioural characteristics of the mixture failure rate of a remanufactured product at different mixture proportions.

Effects of Mixture Proportions and Molding Method on the Performance of Pervious Recycled Aggregate Concrete.

The use of pervious concrete pavement systems with recycled aggregates is a sustainable and innovative solution to major urbanization challenges such as repurposing construction waste, alleviating urban waterlogging, and reducing heat-island effects. This study aims to investigate the effects of mixture proportions and molding methods on the performance of pervious recycled aggregate concrete (PRAC). To this end, the coarse aggregate size (4.75~9.5 mm, 9.5~16 mm, and 16~19 mm), the molding method (layered insertion-tamping and vibration molding with vibration times of 5 s, 10 s, or 15 s, respectively), and the replacement rate of recycled coarse aggregate (RCA) (0%, 30%, 50%, and 100%, respectively) are considered. The results reveal that the addition of RCA to permeable concrete weakens its permeability. However, the compressive strength of PRAC reaches its maximum value when the RCA replacement rate is 50%. A larger aggregate particle size (16~19 mm) enhances the compressive strength of PRAC, yet decreases the permeability of PRAC. By using vibration molding to fabricate PRAC, an extension to the vibration duration increases the compressive strength, yet concurrently decreases the permeability. Based on the compressive strength and permeability coefficient of PRAC, the optimal mixture proportions and molding method are suggested.

Design of a novel ternary blended Self-Compacting Ultra-high-performance Geopolymer Concrete

In this research study, the Taguchi orthogonal array (TOA) optimisation method was used to design the optimum mixture proportions of ternary blended Self-Compacting Ultra-High-Performance Geopolymer Concrete (SCUHPGC). The SCUHPGC mixtures, cured at ambient conditions, included Ground Granulated Blast Furnace Slag (GGBFS), Fly Ash (FA), and Silica Fume (SF) as aluminosilicate sources and sodium hydroxide solution and sodium silicate solution as alkaline activators. The effects of six factors at three levels on the compressive strength of SCUHPGC were investigated. The considered factors were the binder content, the ratio of fly ash to ground granulated blast furnace slag (FA/GGBFS), the ratio of silica fume to binder content (SF/Binder content), the ratio of alkaline activator to binder content (AAL/Binder content), the ratio of sodium silicate to sodium hydroxide (SS/SH), and the concentration of sodium hydroxide solution. Twenty-seven SCUHPGC mixtures were evaluated. The slump flow diameter of the optimum SCHUPGC was 740 mm, which satisfies the requirements of self-compacting concrete. The maximum 28-day compressive strength of 132.7 MPa was achieved by the optimum SCUHPGC mixture, which contained a binder content of 1050 kg/m3, a FA/GGBFS ratio of 0.3, an SF/Binder content ratio of 0.05, an AAL/Binder content ratio of 0.4, a SS/SH ratio of 4.0, and an SH concentration of 12 M. The 28-day flexural strength and tensile strength of the optimum SCUHPGC mixture were 9.6 MPa and 4.9 MPa, respectively.

Applications of Probabilistic Genotyping Method for Combining Evidence Across Microhaplotype DNA Mixture Profiles.

In cases of serious crimes that involve challenging DNA samples from the perpetrator (e.g., a minor contributor to a mixture), there is justification to combine different mixture profiles. In our previous study, we developed a massively parallel sequencing (MPS)-based assay targeting 140 microhaplotype markers. In this study, we extended the use of the microhaplotype panel to common scenarios, such as determining the presence of a common contributor or relatedness between different mixture profiles when no reference source is available. Data interpretation was performed using the R package KinMix. Our findings revealed that correct assignments of a common contributor and relatedness were made between relatively balanced mixtures. However, when profiles suffered from allele imbalance, inclusive assignments were significantly associated with the suspect's mixture proportion. Additionally, our analysis showed zero false-positive rates in the studied scenarios. These results indicate that microhaplotype data can be reliably interpreted for identifying a common donor or related donors among different mixtures. Further research based on larger sample sizes may yield more reliable results, which could assist in solving issues related to complex scenarios where multiple mixed profiles were involved.

Aggregate blending optimisation in quarries to improve of the mechanical and environmental properties of concrete

ABSTRACT In this study, concrete mix optimisation was carried out with materials provided by Catalca (Ca) and Cendere (Ce) quarries in the region of Istanbul, Turkey which produce concrete aggregate from different rock formations. Accordingly, the properties of six different concrete aggregates produced from these quarries (3 regions in Catalca quarry and 3 regions in Cendere quarry) and the amounts of CO2-e released into the atmosphere by producing these aggregates were determined. Then, these aggregates were used in different proportions in concrete mixtures and the effects of aggregate types on the mechanical properties of concrete were statistically demonstrated by the experimental design method. According to the results, the mechanical properties of concrete were statistically significantly affected by the use of aggregate in different formations. In addition, empirical approaches that can be used in estimating the compressive strength, tensile strength and elasticity modulus of concrete have been found and using these approaches, mixtures that maximise these mechanical properties of concrete have been determined. Accordingly, mechanical properties of concrete were maximised in mixtures where Ca 1st Region 25% - Ca 2nd Region 75%, Ca 1st Region 60% - Ca 3rd Region 40%, Ca 2nd Region 13% - Ca 3rd Region 87%, Ce 1st Region 10% - Ce 3rd Region 90%, Ce 1st Region 63% - Ca 2nd Region 37%, and Ca 1st Region 35% - Ca 2nd Region 5% - Ca 3rd Region 60% were used. The CO2-e values of these mixtures were calculated as 8.405, 8.248, 8.529, 8.630, 8.476, and 8.365 kg/t respectively.

Effects of hydrogen enrichment on the performance and emission characteristics of a diesel engine with the addition of Syzygium cumini (Jamun) biodiesel

ABSTRACT Biofuel made from Syzygium cumini, often known as Jamun, has the potential to be a low-cost, sustainable, and emission-free alternative. In this work, we experimentally explore the performance of a compression ignition (CI) engine that burns a blend of hydrogen and biodiesel. Hydrogen is pumped into the air intake manifold by means of a hydrogen gaseous supplement that, when exposed to atmospheric conditions, co-combusts with a pilot flame ignited by Jamun oil. To investigate the impact of hydrogen supplementation on performance and exhaust emission, a variety of hydrogen–Jamun fuel mixture proportions are provided to the engine. The research demonstrates that adding hydrogen improves the thermal efficiency (5%) of diesel engines whereas lowering specific energy fuel consumption (4%) at fixed Jamun flow rates. The gas emission data demonstrates that when hydrogen (HC) emission (12%) supplements decreases, NOx emission (10%) increases somewhat but opacity (8%) increases significantly. The experiment demonstrates that hydrogen and Jamun duel fuel in compression ignition engines burn smoothly.