Relative Deviation Research Articles

Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning

This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg–Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. The MLP network’s determination coefficient (R2) was 0.98 over 30 epochs, and its mean squared error (MSE) was 0.0028. The RBF network also obtained an R2 of 0.98 and an MSE of 0.0026 over 40 epochs. A two-factor interaction design served as the basis for the RSM model, which produced an R2 of 0.91. A comparison of the RSM, MLP, and RBF models, using the average absolute relative deviation, indicated that the ANN models, particularly the RBF model, produced more accurate predictions than the RSM model. The findings show that temperature and concentration were the two most significant factors influencing sulfur removal efficiency. Overall, artificial neural networks outperformed the RSM approach in predicting desulfurization performance, providing a more reliable modeling tool for optimizing the sulfur removal process.

Comparison of two in vitro methods progressed in a computer-controlled simulated digestion system to determine amino acid digestibility of feed ingredients for yellow-feathered roosters.

This experiment compared amino acid (AA) digestibility assessed by 2 in vitro methods using a computer-controlled simulated digestion system and in vivo assay for corn, soybean meal, casein, corn gluten meal, cottonseed meal, rapeseed meal and a corn-soybean meal diet. In vitro method 1 simulated gizzard digestion at pH 2.0, followed by small intestinal digestion, and the subsequent clearance of the digested product from dialysis tubing. In vitro method 2 was similar to the first method, except that pH in gizzard digestion was 3.5 and there was an enzymatic inactivation stage before digested product clearance. Each in vitro method included 5 replicates per treatment, with 1 digestion tube per replicate. Cecectomized Chinese yellow-feathered roosters (average body weight of 2.73 kg) were assigned to 1 of the following treatments: corn, soybean meal, or a corn-soybean meal diet (n = 6 replicates of 3 roosters per treatment); or casein, corn gluten meal, cottonseed meal, or rapeseed meal (n = 5 replicates of 3 roosters per treatment) in a completely randomized design to assess digestibility of AA. The relative deviation was within 5% comparing methods 1, 2 and in vivo method for 87.5% and 92.0% of all AA digestibility measured of 7 samples, respectively. Significant linear relationships were observed between in vitro methods 1 and 2 for the digestibility of 14 AAs (except for Cys) and total amino acid (TAA) (r ≥ 0.778; P < 0.05). Significant linear relationships were found between in vitro method 1 and in vivo results for 9 AAs (except for His, Ile, Asp, Cys, Glu and Ser) and TAA (r ≥ 0.866; P < 0.05). Similarly, significant linear relationships between in vitro method 2 and in vivo findings were observed for 11 AAs (except for His, Lys, Cys and Glu) and TAA (r ≥ 0.776; P < 0.05). The linear regression of in vivo assay on in vitro method 1 or 2 overlapped with Y = X for 7 AA and TAA or 10 AA and TAA, respectively. Our findings suggest in vitro method 2 is superior to method 1 for estimating AA digestibility of yellow-feathered roosters, this indicates that gizzard pH or inactivation of enzymes modulates the effectiveness of in vitro digestibility assays.

An Environmental Engineering Study Case: Constructing Cataluminescence Sensors Based on Octahedral Nanocomposites for Isovaleraldehyde Detection

Isovaleraldehyde is an important chemical raw material for the production of flavors, which is volatile and flammable and poses a health risk to humans. It is, therefore, essential to develop a rapid assay for the identification of isovaleraldehyde. In this study, octahedral NiCo2O4/MIL-Fe53 nanocomposites were successfully fabricated for the rapid detection of isovaleraldehyde. The prepared NiCo2O4/MIL-Fe53 nanocomposites were characterized by SEM, XRD, FTIR, and XPS to analyze the material properties. The effects of temperature, carrier gas flow rate, selectivity, and stability on the cataluminescence performance of this sensor were investigated. The results showed that NiCo2O4/MIL-Fe53 nanocomposites have excellent selectivity to isovaleraldehyde with response and recovery times of 6 and 8 s, respectively. A linear relationship was found between the CTL signal and isovaleraldehyde concentration Y = 9.56X − 23.3 (R2 = 0.99) over the concentration range of 13.66 to 437.22 ppm with a detection limit of 2.44 ppm. The relative deviation RSD = 4.18% for multiple tests of the sensor indicates good stability and longevity. Mechanistic studies have shown that the heterojunction formed by NiCo2O4/MIL-Fe53 nanocomposites has the advantage of improving CTL sensing performance. This study may advance the application of cataluminescence sensors in the detection of isovaleraldehyde.

Experimental study of the hydraulic and aeration characteristics within the vortex drop shaft spillway

This study focuses on a novel type of vortex drop shaft spillway, addressing the lack of comprehensive research on its hydraulic and aeration characteristics. A combination of experimental and theoretical methods was employed to investigate these characteristics in detail. The results reveal that the relationship between the swirl angle and the Froude number (Frx) remains unclear. However, a positive correlation was observed between the water layer thickness, flow velocity, and Frx. Analytical formulas derived for the swirl angle and water layer thickness showed a small relative deviation from experimental data, and the maximum relative differences are about 4% and 8%, respectively. Furthermore, a linear negative correlation was identified between vortex cavity length, relative aeration rate (β), and Frx. An empirical relationship between β and Frx was established. When Frx ranges from 0.064 to 0.89, the aeration concentration along the shaft gradually decreases, while it increases along the outlet tunnel. At lower Frx values, the transverse aeration concentration in the upper layer of the outlet tunnel is highest at the center and lower at the sides. In the lower layer, the concentration is higher at the sides and lower at the center. The aeration concentration near the wall of the outlet tunnel is more than 4%, which can effectively reduce the cavitation damage in the outlet tunnel. These findings deepen the understanding of the hydraulics and aeration mechanism in the vortex drop shaft spillway, offering theoretical support for its application and further development.

Mesalazine solubility in supercritical carbon dioxide with and without cosolvent and modeling

In this study, the solubility of mesalazine in supercritical carbon dioxide with and without cosolvent was carried out for the first time at different temperatures and pressure values ranging from 308 to 338 K and 12 to 30 MPa, respectively. The determined experimental molar solubilities of mesalazine in supercritical carbon dioxide were in the range of 4.41 × 10–5 to 9.97 × 10–5 (308 K), 3.9 × 10–5 to 13.1 × 10–5 (318 K), 3.4 × 10–5 to 16 × 10–5 (328 K) and 3.3 × 10–5 to 18.4 × 10–5 (338 K). Meanwhile, the determined experimental molar solubilities in supercritical carbon dioxide using 2% dimethyl sulfoxide as cosolvent were in the range of 28.22 × 10–5 to 36.2 × 10–5 (308 K), 26.07 × 10–5 to 51.41 × 10–5 (318 K), 25.02 × 10–5 to 69.07 × 10–5 (328 K) and 25.86 × 10–5 to 82.6 × 10–5 (338 K). A novel association model was employed to simulate the solubility data of the binary and ternary systems. Various semiempirical correlations were utilized to calculate the solubility of mesalazine in supercritical carbon dioxide. The new association model was deemed the most superior model, achieving an average absolute relative deviation value of 4.13% without a cosolvent, and 3.36% when a cosolvent was included.

A Quantitative Basis for Intersource Differences in Tracer Concentrations for Accurate Sediment Source Fingerprinting

A Quantitative Basis for Intersource Differences in Tracer Concentrations for Accurate Sediment Source Fingerprinting

ИССЛЕДОВАНИЕ РАБОТЫ ЛОПАСТНОГО СМЕСИТЕЛЯ СЫПУЧИХ РАСТИТЕЛЬНЫХ КОМПОНЕНТОВ В ЭКСТРЕМАЛЬНЫХ РЕЖИМАХ

The purpose of the study is to compare the results of theoretical and experimental studies of the developed and patented design of a paddle mixer in the field of applicability of the theory of mixing loose plant components proposed by the authors. Objectives: to carry out experimental studies of the parametric area of applicability of the theory of mixing with an assessment of the discrepancy between theoretically predicted and actual values of variability and energy intensity in the vicinity of their optimum in order to adapt the model to production conditions. Theoretical estimates of the result indicators and their comparison with similar experimentally obtained values were made using the author's computer program for the process of mixing bulk plant components. For the theoretical description of the boundaries of the region, the Maple computer mathematics system was used in the range of result indicators from minimum to maximum. Research checks were carried out to determine the performance indicators of the paddle mixer operation: the variability and energy intensity of the process of mixing bulk plant components when choosing the optimal modes in the allowable parametric region specified by the angular speed of the shaft rotation, the angle of the blades and the content of millet in the mixture. The quality of the mixing process under the conditions of choosing the best modes of operation of the mixer is estimated by the relative deviation of the actual value of the result indicator from the similar optimal value. It has been established that with the calculation format used, the indicated discrepancy between the minima and maxima does not exceed 5%: for variability it is 2.64832 and 0.36521%, respectively, and for energy intensity it is 0.35019 and 1.34564%.

Research on an equivalent calculation method of the sealing structure of subsea pipeline connectors based on dimensionality reduction method

For the sealing structure of subsea pipeline connectors, it is crucial to understand the maximum equivalent stress of the sealing ring, the normal force generated by the pressure effect of the internal fluid acting on the contact area of the flanges and the sealing ring, and the displacement of the flange in the pre-tightening state and the working state. This paper proposes an equivalent calculation method (ECM) to quickly compute the values of these three variables. A theoretical model for the parallel contact of the axially symmetric object and an elastic foundation is proposed based on the dimensionality reduction method and Hertzian theory. Considering the contact and structural characteristics of the flanges and the sealing ring of the sealing structure, an equivalent hollow cylinder model is introduced. By combining these two models, along with the long hollow cylinder theory, theoretical formulas for the maximum equivalent stress, the normal force, and the flange displacement in the pre-tightening state and the working state of the sealing structure are derived. In addition, finite element simulations were conducted on the sealing structure of different sizes and working states. A comparative analysis was performed between the simulation results and the theoretical formulas results. The results show that, within the yield strength of the sealing ring, the maximum relative deviation of the three theoretical formulas is less than 10%. The theoretical formulas can be used for the design and engineering application of the sealing structure of subsea pipeline connectors.

Symbolic-Regression Aided Development of a New Cubic Equation of State for Improved Liquid Phase Density Calculation at Pressures Up to 100 MPa

For over a century, cubic equations of state (EoS) have been used to calculate density and phase equilibria of pure fluids and mixtures. Despite a century’s development with hundreds of resulting cubic EoS, their accuracy in liquid phase density calculations is still unsatisfactory. In this work, a new cubic EoS was developed to improve the accuracy of liquid phase density calculation while keeping similar accuracy of other properties. The new cubic EoS, named YFR (Yang-Frotscher-Richter) EoS, was developed based on the functional form of the Patel–Teja (PT) EoS [p = RT/(v − b) − a/(v(v + b) + c(v − b)]. In the PT EoS, parameters b and c are linked to an empirical critical compressibility factor ξc, and all these three parameters are constants for a pure fluid. By contrast, in the YFR EoS, ξc, b, and c are functions of temperature, and the equations describing this dependency were developed with symbolic regression. This is the key to improving liquid phase density calculation, although it leads to thermodynamic inconsistencies at high pressures. The application range of the new cubic EoS is thus limited to pressures up to 100 MPa. The YFR EoS was developed using nearly all pure fluids available in NIST’s REFPROP 10.0 database, with reference values computed with REFPROP. The average of the absolute value of relative deviations (AARD) of liquid phase densities calculated with the YFR EoS from reference values is approximately 2 %, compared to 3 % when using the Patel–Teja–Valderrama (PTV) EoS and 6 % when using the Peng-Robinson (PR) EoS. The YFR EoS has been implemented in our self-developed OilMixProp 1.0 software package.

Passive fast neutron multiplicity counting system based on a symmetric spherical design

ABSTRACT The measurement results of current Fast Neutron Multiplicity Counting (FNMC) systems are susceptible to the size and shape factors of special nuclear materials under test, primarily due to the sample position or size dependence of the detection efficiencies of these systems. To reduce this effect, a symmetric spherical design for a FNMC system is proposed. Two systems made of 32 and 16 liquid scintillation detectors were simulated by Geant4, and the spatial uniformities of the detection efficiencies of the two systems were characterized. Compared with the system made of 16 liquid scintillation detectors, the detection efficiency of the system made of 32 liquid scintillation detectors is more uniform, due to the solid angle covered by 32 detectors is larger than that covered by 16 detectors. The relative deviations (RDs) of its detection efficiencies within a spherical radius of 6 cm in the system are less than ± 3.3%. Thus, a FNMC system made of 32 liquid scintillation detectors was built. Several 252Cf neutron sources were placed on the sample platform with different spatial distributions to characterize the spatial uniformity of the detection efficiencies of the system. And their spontaneous fission (SF) rates were calculated using the FNMC analytical equation. The results show that the RDs of the detection efficiencies within a radius of 6 cm in the system are less than ± 2%, and the RDs of the estimated SF rates of the neutron sources are less than ± 1.8%.

A simple algorithm to derive virtual non-contrast electron density from dual-energy computed tomography data for radiotherapy treatment planning.

The use of iodinated contrast-enhancing agents in computed tomography (CT) improves the visualization of relevant structures for radiotherapy treatment planning (RTP). However, it can lead to dose calculation errors by incorrectly converting a CT number to electron density. This study aimed to propose an algorithm for deriving virtual non-contrast (VNC) electron density from dual-energy CT (DECT) data. This algorithm was developed by extending the formula previously developed by Saito, which enables the calculation of the electron density of human tissue through weighted subtraction of CT numbers acquired from DECT scans. To investigate the feasibility of the proposed VNC algorithm, we performed analytical DECT image simulations at 90 and 150kV/Sn on virtual phantoms consisting of various tissue/iodine surrogates with known mass densities and elemental compositions. Two different shapes of phantoms made of water-mimicking surrogates were generated as inputs: a circular phantom (33cm diameter) for calibration and an elliptical phantom (33cm width and 28cm height) for validation. The circular phantom was equipped with inserts of human-tissue-mimicking substitutes, pure water, and iodine-enhanced soft-tissue substitutes (2, 5, 10, and 15mg/mL iodine). The elliptical phantom contained inserts of reference human tissues, iodine-enhanced soft-tissue substitutes (2, 2.5, 5, 7.5, 10, 15, and 20mg/mL iodine), and a 10-mm-diameter core of 4mg/mL iodine surrounded by a blood-mimicking base material. The performance of the proposed algorithm was evaluated by comparing the accuracy of VNC electron densities with those of non-contrast (NC) base materials (water- or blood-mimicking surrogates). The derived algorithm enabled the calculation of VNC electron density in a manner similar to that of unenhanced human tissues by adapting a VNC-specific weighting factor, thereby eliminating the intermediate step of converting CT numbers to electron density. The simulated results showed that the VNC algorithm could almost completely remove the contrast in the electron density image between iodine-enhanced and base materials. The relative deviations of simulated VNC electron density values from the corresponding pre-contrast value were within±0.4% for all tested materials, with a root-mean-square error (RMSE) of 0.2%. Within the limits of the analytical DECT image simulation used in this study, the simple VNC algorithm could effectively provide accurate VNC electron densities for iodine-enhanced materials. This may allow the contrast agent to be used for CT scans during RTP without compromising dose calculation accuracy.

One-week test-retest stability of heart rate variability during rest and deep breathing.

Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions. &#xD;This observational study includes healthy individuals. A 20-minute electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CV) were used to assess the concordance between the two measurements and the relative deviation, respectively.&#xD;From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in RMSSD were not sufficiently explained by lifestyle factors. &#xD;Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.

Temperature-dependent thermodynamic predictions for N,N-dimethylbenzylamine + pentanol systems using the Jouyban-Acree equation (active amyl alcohol, 2-pentanol and 2-methyl-2-butanol)

ABSTRACT Excess molar volume, excess isentropic compressibility, deviation in viscosity and excess Gibbs free energy of activation of viscous flow for binary mixtures of N,N-dimethyl benzyl amine with 2-methyl-1-butanol(P1), 2-pentanol (P2) and 2-methyl-2-butanol (P3) components selected compositions were determined from the measured values of densities (ρ), viscosities (n), and speeds of sound (u) of pure components and their mixtures from 303.15 K to 313.15 K. The results are analysed in terms of intermolecular interactions or hydrogen bonding or netero-association interactions in the binary mixtures. Finally, the Prigogine-Flory-Patterson (PFP) Theory is applied to identify the most predominant molecular interaction. Using the Jouyban-Acree model, results are discussed in terms of mean relative deviation (MRDs) and individual relative deviation (IRD) between calculated and experimental densities, speeds of sound and viscosities as an accuracy criterion.

δ26Mg Values of Thirty‐One Geological Reference Materials Analysed by the Critical Mixture Double Spike Technique

Stable Mg delta values (the relative deviation from a certain reference material, e.g., DSM‐3 here and expressed as δ26MgDSM‐3) were routinely measured by the sample‐standard bracketing (SSB) method on a multi‐collector ICP‐MS, as only three isotopes (i.e., 24Mg, 25Mg and 26Mg) naturally exist. Due to potential inaccuracy in correcting mass bias during measurements, considerable measurement bias has been reported among laboratories. Recently, a critical mixture double spike (CMDS) technique has been developed and demonstrated to accurately correct mass bias of measurement results with precision of ± 0.03‰ for δ26Mg. Here, we measured thirty‐one geological reference materials including igneous, metamorphic and sedimentary rocks, sediments and minerals using the CMDS technique, with the purpose of better characterising their δ26MgDSM‐3 values. Aligning with the data previously reported, uncorrected bias, on average measured by ∆26MgSSB‐CMDS (i.e., δ26MgSSB ‐ δ26MgCMDS) as ‐0.071 ± 0.092‰ (2s, n = 42), has been reaffirmed for the traditional SSB method. Such uncorrected bias positively correlates with sample Mg/(Si+Al+Ca), and thus may result from the accumulative effect of residual matrix elements. The new data set herein can aid future inter‐laboratory comparison and data quality control.

Development of a CFD simulation for the analysis of CO2 separation percentage using novel [emim][C2N3] ionic liquid solution inside the gas–liquid contactor

Growing emission of environmentally-hazardous greenhouse pollutants (especially CO2) has motivated the researchers to apply gas–liquid membrane contactors as an easy-to-operate and cost-effective technique for increasing their separation efficiency from different sources. In the current decades, ionic liquids (ILs) have shown their potential in the gas separation industry owing to their noteworthy advantages such as great capacity, excellent adjustability and suitable thermal/chemical stability compared to commonly-employed amine absorbents. This investigation aims to analytically/numerically determine the separation yield of CO2 from CO₂/N2 gaseous flow using novel -Ethyl-3-methylimidazolium dicyanamide ([emim][C2N3]) IL inside the gas–liquid contactor. To fulfill the ultimate purpose, a CFD simulation has been proposed using COMSOL Multiphysics software to predict the results. Comparison of model outcome with experimental data has shown brilliant concurrence with the average relative deviation of almost 5%. Evaluation of the results has shown the excellent performance of [emim][C2N3] IL for the removal of CO2 (Separation efficiency of around 100%). Finally, the effects of some module/membrane parameters on increasing or decreasing the separation efficiency has been studies in detail.

PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy.

To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.

Experimental and theoretical investigation of thermodynamic and transport properties in binary mixtures at various temperatures (dibenzyl amine with 1-pentanol, 2-pentanol and 2-methyl-2-butanol)

ABSTRACT Excess molar volume, excess isentropic compressibility, deviation in viscosity and excess Gibbs free energy of activation of viscous flow for binary mixtures of dibenzyl amine with 1-pentanol (P1), 2-pentanol (P2) and 2-methyl-2-butanol (P3) components selected compositions were determined from the measured values of densities (ρ), viscosities (η), and speeds of sound (u) of pure components and their mixtures from 303.15 K to 313.15 K. The results are analysed in terms of complex formation through proton donor – acceptor interactions or hydrogen bonding or hetero molecular association interactions in the binary mixtures. Finally, the Prigogine–Flory–Patterson (PFP) Theory is applied to identify the most predominant molecular interaction. Jouyban-Acree model, results are discussed in terms of mean relative deviation (MRDs) and individual relative deviation (IRD) between calculated and experimental densities, speeds of sound and viscosities as an accuracy criterion.

Rapid Identification of Saline–Alkali Stress-Tolerant Peanut Varieties Based on Multimodal Data

The cultivation of saline–alkali-tolerant peanut (Arachis hypogaea L.) varieties can effectively increase grain yield in saline–alkali land. However, traditional assessment methods are often cumbersome and time consuming. To rapidly identify saline–alkali stress-tolerant peanut varieties, this research proposed a saline–alkali stress tolerance evaluation method based on deep learning and multimodal data. Specifically, the research first established multimodal datasets for peanuts at different growth stages and constructed a saline–alkali stress score standard based on unsupervised learning. Subsequently, a deep learning network called BO-MFFNet was built and its structure and hyperparameters were optimized by the Bayes optimization (BO) algorithm. Finally, the point prediction of the saline–alkali stress score were carried out by using the Gaussian process regression model. The experimental results show that the multimodal method is superior to the single-modal data and the BO algorithm significantly improves the performance of the model. The root mean squared error and relative percentage deviation of the BO-MFFNet model are 0.089 and 3.669, respectively. The model effectively predicted the salt–alkali stress tolerance of five varieties, and the predicted results were Huayu25, Yuhua31, Yuhua33, Yuhua32, and Yuhua164 from high to low. This research provides a new method for assessing crop tolerance under extreme environmental stress.

Characteristics of NO/SO2 generation during coal/steel dust co-firing under multi-factor synergies

ABSTRACT The co-combustion of solid waste and coal in industrial kilns represents the primary method for the treatment of solid waste. This process must address the critical issue of efficiently controlling nitrogen oxides (NO) and sulfur dioxide (SO2) emissions. In this study, we initially conducted combustion experiments using both single-fuel and mixed-fuel configurations involving coal and steel dust. The experimental results indicated a positive correlation between temperature, airflow rate, and NO/SO2 emissions, while the mixing ratios exhibited a negative correlation. A straightforward regression prediction model for NO/SO2 emissions was developed to enhance understanding of the generation characteristics of NO and SO2, notably, the relative deviation from experimental results was merely 3.16%. This model integrates fuel properties with combustion conditions to elucidate the emission characteristics of nitrogen oxides (NO) and sulfur dioxide (SO2) at their source. Finally, Response surface methodology (RSM) was applied to analyze NO/SO2 emissions under varying operating conditions, including temperature (700–900 °C), air flow rate (80–200 mL/min), and mixing ratios (10–50%).The optimal results were observed at a blending ratio of 50%, where nitrogen (N) and sulfur (S) conversion rates decreased by 25% and 23%, respectively. The results of the analyses determined that the key factors affecting NO emissions, in order of influence, are the mixing ratio, temperature, and air flow rate. For SO2 emissions, the order is mixing ratio, air flow rate, and temperature.

Production scheduling with multi-robot task allocation in a real industry 4.0 setting

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.