Exponential Response Research Articles

Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models

This article proposes an adaptively bounded gradient descent (ABGD) algorithm for group elastic net penalized regression. Unlike previously proposed algorithms, the proposed algorithm adaptively bounds the Fisher information matrix, which results in a flexible and stable computational framework. In particular, the proposed algorithm (i) does not require orthogonalization of the predictors, and (ii) can be easily applied to any combination of exponential family response distribution and link function. The proposed algorithm is implemented in the grpnet R package (available from CRAN), which implements the approach for common response distributions (Gaussian, binomial, and Poisson), as well as several response distributions not previously considered in the group penalization literature (i.e., multinomial, negative binomial, gamma, and inverse Gaussian). Simulated and real data examples demonstrate that the proposed algorithm is as or more efficient than existing methods for Gaussian, binomial, and Poisson distributions. Furthermore, using two genomic examples, I demonstrate how the proposed algorithm can be applied to high-dimensional multinomial regression problems with grouped predictors. R code to reproduce the results is included as supplementary materials.

Analytical and numerical modelling of the electrostatic behaviour of highly insulating materials in the time domain

The usual response of an insulator subjected to a voltage step involves time power laws. We present here mathematical tools allowing to calculate this time domain response in open circuit after an initial charge deposit, within the framework of linear systems theory, using linear fractional transfer functions. In the time domain, the inverse Laplace transform of the data taken from the frequency domain leads to Mittag-Leffler functions, generalizing the Debye exponential response to an extended fractional α-order response. The open-circuit boundary conditions are different from the closed-circuit ones. We nevertheless demonstrate that using a transfer function deduced from the Cole-Cole response in closed-circuit, a precise analytical formula of the potential decay after an initial charge deposit may be established, and a numerical computation of this decay may be performed using easily available software. Applying the superposition principle, the voltage return following a brief short circuit may also be deduced. Experimental results are presented and the limits of the superposition principle applied to real materials are discussed.

Stronger compensatory thermal adaptation of soil microbial respiration with higher substrate availability.

Ongoing global warming is expected to augment soil respiration by increasing the microbial activity, driving self-reinforcing feedback to climate change. However, the compensatory thermal adaptation of soil microorganisms and substrate depletion may weaken the effects of rising temperature on soil respiration. To test this hypothesis, we collected soils along a large-scale forest transect in eastern China spanning a natural temperature gradient, and we incubated the soils at different temperatures with or without substrate addition. We combined the exponential thermal response function and a data-driven model to study the interaction effect of thermal adaptation and substrate availability on microbial respiration and compared our results to those from two additional continental and global independent datasets. Modeled results suggested that the effect of thermal adaptation on microbial respiration was greater in areas with higher mean annual temperatures, which is consistent with the compensatory response to warming. In addition, the effect of thermal adaptation on microbial respiration was greater under substrate addition than under substrate depletion, which was also true for the independent datasets reanalyzed using our approach. Our results indicate that thermal adaptation in warmer regions could exert a more pronounced negative impact on microbial respiration when the substrate availability is abundant. These findings improve the body of knowledge on how substrate availability influences the soil microbial community-temperature interactions, which could improve estimates of projected soil carbon losses to the atmosphere through respiration.

Inverter PQ Control With Trajectory Tracking Capability for Microgrids Based on Physics-Informed Reinforcement Learning

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach known as the twin delayed deeper deterministic policy gradient. With the model-based derivation, the learning space of the RL agent is narrowed down from a function space to a real space, which reduces the training complexity significantly. Finally, the proposed method is verified through numerical simulation in MATLAB-Simulink and power HIL experiments in the CURENT center.With the physics-informed learning method, exponential response time constants can be freely assigned to IBRs, and they can follow any predefined trajectory without complicated gain tuning.

Using the softplus function to construct alternative link functions in generalized linear models and beyond

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For example, the exponential response function is often assumed for parameters restricted to be positive, although it implies a multiplicative model, which is not necessarily desirable or adequate. Consequently, applied researchers might face misleading results when relying on such defaults. For parameters restricted to be positive, we propose to construct alternative response functions based on the softplus function. These response functions are differentiable and correspond closely to the identity function for positive values of the regression predictor implying a quasi-additive model. Consequently, the proposed response functions allow for an additive interpretation of the estimated effects by practitioners and can be a better fit in certain data situations. We study the properties of the newly constructed response functions and demonstrate the applicability in the context of count data regression and Bayesian distributional regression. We contrast our approach to the commonly used exponential response function.

Punctuated chaos and indeterminism in self-gravitating many-body systems

Dynamical chaos is a fundamental manifestation of gravity in astrophysical, many-body systems. The spectrum of Lyapunov exponents quantifies the associated exponential response to small perturbations. Analytical derivations of these exponents are critical for understanding the stability and predictability of observed systems. This paper presents a new model for chaos in systems with eccentric and crossing orbits. Here, exponential divergence is not a continuous process but rather the cumulative effect of an ever-increasing linear response driven by discrete events at regular intervals, i.e. punctuated chaos. We show that long-lived systems with punctuated chaos can magnify Planck length perturbations to astronomical scales within their lifetime, rendering them fundamentally indeterministic.

Impact of iron fertilisation on atmospheric CO2 during the last glaciation

Abstract. While several processes have been identified to explain the decrease in atmospheric CO2 during glaciations, a better quantification of the contribution of each of these processes is needed. For example, enhanced aeolian iron input into the ocean during glacial times has been suggested to drive a 5 to 28 ppm atmospheric CO2 decrease. Here, we constrain this contribution by performing a set of sensitivity experiments with different aeolian iron input patterns and iron solubility factors under boundary conditions corresponding to 70 000 years before present (70 ka), a time period characterised by the first observed peak in glacial dust flux. We show that the decrease in CO2 as a function of Southern Ocean iron input follows an exponential decay relationship. This exponential decay response arises due to the saturation of the biological pump efficiency and levels out at ∼21 ppm in our simulations. We show that the changes in atmospheric CO2 are more sensitive to the solubility of iron in the ocean than the regional distribution of the iron fluxes. If surface water iron solubility is considered constant through time, we find a CO2 drawdown of ∼4 to ∼8 ppm. However, there is evidence that iron solubility was higher during glacial times. A best estimate of solubility changing from 1 % during interglacials to 3 % to 5 % under glacial conditions yields a ∼9 to 11 ppm CO2 decrease at 70 ka, while a plausible range of CO2 drawdown between 4 to 16 ppm is obtained using the wider but possible range of 1 % to 10 %. This would account for ∼12 %–50 % of the reconstructed decrease in atmospheric CO2 (∼32 ppm) between 71 and 64 ka. We further find that in our simulations the decrease in atmospheric CO2 concentration is solely driven by iron fluxes south of the Antarctic polar front, while iron fertilisation elsewhere plays a negligible role.

Modeling IP[formula omitted]-induced Ca[formula omitted] signaling based on its interspike interval statistics

Inositol 1,4,5-trisphosphate (IP3)-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. Recent research demonstrated randomness of Ca2+ signaling on all structural levels. We compile eight general properties of Ca2+ spiking common to all cell types investigated and suggest a theory of Ca2+ spiking starting from the random behavior of IP3 receptor channel clusters mediating the release of Ca2+ from the endoplasmic reticulum capturing all general properties and pathway-specific behavior. Spike generation begins after the absolute refractory period of the previous spike. According to its hierarchical spreading from initiating channel openings to cell level, we describe it as a first passage process from none to all clusters open while the cell recovers from the inhibition which terminated the previous spike. Our theory reproduces the exponential stimulation response relation of the average interspike interval Tav and its robustness properties, random spike timing with a linear moment relation between Tav and the interspike interval SD and its robustness properties, sensitive dependency of Tav on diffusion properties, and nonoscillatory local dynamics. We explain large cell variability of Tav observed in experiments by variability of channel cluster coupling by Ca2+-induced Ca2+ release, the number of clusters, and IP3 pathway component expression levels. We predict the relation between puff probability and agonist concentration and [IP3] and agonist concentration. Differences of spike behavior between cell types and stimulating agonists are explained by the different types of negative feedback terminating spikes. In summary, the hierarchical random character of spike generation explains all of the identified general properties.

Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary

In this article we present a space–time epidemic-type aftershock sequence (ETAS) model for the area of Hungary, motivated by the goal of its application in insurance risk models. High-quality recent instrumental data from the period 1996–2021 are used for model parameterization, including data from the recent nearby Zagreb and Petrinja event sequences. In the earthquake-triggering equations of our ETAS model, we replace the commonly used modified Omori law with the more recently proposed stretched exponential time response form, and a Gaussian space response function is applied with a variance add-on for epicenter error. After this model was tested against the observations, an appropriate overall fit for magnitudes M≥3.0 was found, which is sufficient for insurance applications, although the tests also show deviations at the M=2.5 threshold. Since the data used for parameterization are dominated by Croatian earthquake sequences, we also downscale the model to regional zones via parameter adjustments. In the downscaling older historical data are incorporated for a better representation of the key events within Hungary itself. Comparison of long-term large event numbers in simulated catalogues versus historical data shows that the model fit by zone is improved by the downscaling.

Estimation in exponential family regression based on linked data contaminated by mismatch error

Identification of matching records in multiple files can be a challenging and error-prone task. Linkage error can considerably affect subsequent statistical analysis based on the resulting linked file. Several recent papers have studied post-linkage linear regression analysis with the response variable in one file and the covariates in a second file from the perspective of the "Broken Sample Problem" and "Permuted Data". In this paper, we present an extension of this line of research to exponential family response given the assumption of a small to moderate number of mismatches. A method based on observation-specific offsets to account for potential mismatches and $\ell_1$-penalization is proposed, and its statistical properties are discussed. We also present sufficient conditions for the recovery of the correct correspondence between covariates and responses if the regression parameter is known. The proposed approach is compared to established baselines, namely the methods by Lahiri-Larsen and Chambers, both theoretically and empirically based on synthetic and real data. The results indicate that substantial improvements over those methods can be achieved even if only limited information about the linkage process is available.

General relativistic effects and the near-infrared and X-ray variability of Sgr A* I

The near-infrared (NIR) and X-ray emission of Sagittarius A* shows occasional bright flares that are assumed to originate from the innermost region of the accretion flow. We identified 25 4.5 μm and 24 X-ray flares in archival data obtained with the Spitzer and Chandra observatories. With the help of general relativistic ray-tracing code, we modeled trajectories of “hot spots” and studied the light curves of the flares for signs of the effects of general relativity. Despite their apparent diversity in shape, all flares share a common, exponential impulse response, a characteristic shape that is the building block of the variability. This shape is symmetric, that is, the rise and fall times are the same. Furthermore, the impulse responses in the NIR and X-ray are identical within uncertainties, with an exponential time constant τ ∼ 15 m. The observed characteristic flare shape is inconsistent with hot-spot orbits viewed edge-on. Individually modeling the light curves of the flares, we derived constraints on the inclination of the orbital plane of the hot spots with respect to the observer (i ∼ 30° , < 75°) and on the characteristic timescale of the intrinsic variability (a few tens of minutes).

A comparative study of hybrid bit over roller cone and PDC bits

Early hybrid drilling concepts date back to the 1930s, but the development of a viable drilling tool has only become feasible with recent advances in Polycrystalline Diamond Compact (PDC) cutter technology. This paper describes a new generation of hybrid bits based on proven PDC bit designs with roll cutters on the bit periphery. Laboratory and field results will be presented, comparing the performance of hybrid bits with that of conventional PDC and roller bits. A hybrid bit can drill shale and other plastically behaving formations two to four times faster than a roller cone because it is more aggressive and efficient. The penetration rate of a hybrid bit responds linearly to Revolutions Per Minute (RPM), unlike roller taper bits that exhibit an exponential response with an exponent less than one. In other words, a hybrid drill will drill significantly faster than a comparable roller drill in motor applications. Another advantage is the influence of rolling mills on bit dynamics. Compared to conventional PDC bits, torsional oscillations are up to 50% lower, and low-speed slip/slip and high-speed swirl are reduced. This gives the hybrid drill a wider operating window and greatly improves tool face control in directional drilling. The Hybrid Drill is a highly specific drill aimed at traditional cylindrical taper applications limited by the Rate of Penetration (ROP), large-diameter PDC-bit and roller-cone-bit applications limited by torque or Weight on Bit (WOB). Highly interconnected formations with high torque fluctuations can cause premature C3 failure and limit mean operating value torque, and motor and/or P applications where higher ROP and better build speed and tool face control are desired.

Selective Outdoor Humidity Monitoring Using Epoxybutane Polyethyleneimine in a Flexible Microwave Sensor

The rise of gas-sensing applications and markets has led to microwave sensors associated to polymer-based sensitive materials gaining a lot of attention, as they offer the possibility to target a large variety of gases (as polymers can be easily functionalised) at ultra-low power and wirelessly (which is a major concern in the Internet of Things). A two-channel microstrip sensor with one resonator coated with 1,2 epoxybutane-functionalised poly(ethyleneimine) (EB-PEI) and the other left bare was designed and fabricated for humidity sensing. The sensor, characterised under controlled laboratory conditions, showed exponential response to RH between 0 and 100%, which is approximated to −1.88 MHz/RH% (−0.03 dB/RH%) and −8.24 MHz/RH% (−0.171 dB/RH%) in the RH ranges of 30–80% and 80–100%, respectively. This is the first reported use of EB-PEI for humidity sensing, and performances, especially at high humidity level (RH > 80%), as compared with transducer working frequencies, are better than the state of the art. When further tested in real outdoor conditions, the sensor shows satisfying performances, with 4.2 %RH mean absolute error. Most importantly, we demonstrate that the sensor is selective to relative humidity alone, irrespective of the other environmental variables acquired during the campaign (O3, NO, NO2, CO, CO2, and Temperature). The sensitivities obtained outdoors in the ranges of 50–70% and 70–100% RH (−0.61 MHz/%RH and −3.68 MHz/%RH, respectively) were close to lab results (−0.95 MHz/%RH and −3.51 MHz/%RH, respectively).

Stochastic Model of Emotional Stress Development in the Educational Process

The aim of the study is the development of mathematical models that describe the reaction of the individual to the occurrence of stresses of various nature, including those that appear during the implementation of the educational process. The complexity of developing such a model is confirmed by the lack of theoretical results to substantiate the classic experiments of Holmes and Rae on the effect of stress on personality. In this regard, the task is to develop such a mathematical model that would allow not only to give a theoretical explanation of the experimental results used in the Holmes and Rae stress calculator, but also to become a tool for studying the effect of stress on a person in other conditions, including in the process of educational activities of both the teacher and the learner.The research method consists in a mathematical description of the process of occurrence of stresses that develop over time, and it is believed that stresses occur at random times and are characterized by relative stress values indicated in the classical table of Holmes and Rae. The need to involve these results is that they allow us to confirm the correspondence of the developed theoretical mathematical models to already known practical results. The following main assumptions are accepted in the paper. It is believed that a person is exposed to stresses that can occur at random localized points in time and are interpreted as a sequence of points on the time axis, the number and location of which is random. The response of a person to a particular stress is described by a decreasing exponential function of three arguments – current time, random time of stress occurrence, and stress magnitude. The reaction of the individual to a sequence of stresses is the sum of the responses of the person to individual stresses, i.e. it is assumed that the personality exhibits the properties of linearity. In the process of developing a mathematical model, the distribution of the number of random stresses is substantiated according to the Poisson law, which is used to describe the occurrence of random events with a distinct discreteness. The paper introduces one of the key indexes - the coefficient of emotional load, equal to the ratio of the mathematical expectation of everyday stressful background and the mathematical expectation of the sum of this background and additional stress. The response of a person to a particular stress is described by an exponential response function widely used in natural science applications. The total relative value of processed, experienced, random relative values of stress, as well as their non-random mathematical expectations, is introduced into consideration.The new results of the study are: – development of a stochastic mathematical model for the development of stresses over time, depending on the parameters included in the model. It is shown that the behavior of a person’s reaction to stress, predicted by a mathematical model, corresponds to the previously mentioned experimental results. – a study of the behavior in time of a person’s reaction to stress for a situation that was not previously considered and in which the person is affected by a single stress of great intensity, as well as regular emotional imposition.In conclusion, it is noted that the developed model not only makes it possible to theoretically explain the experimental data, but also significantly expand the scope of the studied effect of stress on personality. Thus, it turned out to be possible to predict the impact of a single impact, as well as to indicate a way to account for periodic intentional exposure (emotional suppression). In addition, the results can be used in the study of emotional stresses in the educational process in order to predict and consider them in practical activities.

Sample size and Shapiro-Wilk test: An analysis for soybean grain yield

Ignoring the violation of the error normality assumption is a common mistake that can cause inconsistencies in the results of the analysis of variance. To avoid this, normality tests such as the Shapiro-Wilk test can be used to verify whether this assumption is met or violated. However, the test accuracy relies on a representative number of observations sampled in trials, once the calculated W statistic used for the test is considered sensitive or capable of being altered according to the sample size. Therefore, the aims of this study were to analyze the Shapiro-Wilk test response to sample size for soybean grain yield and to define the sample size for optimizing the W statistic estimate, making it more efficient and reliable. For this, the replacement sampling method was applied in trials that measured soybean grain yield per plant. The trials were performed in two locations in the state of Rio Grande do Sul, Brazil, and in each, three sowing dates were assessed, using a total of 30 commercial soybean cultivars. Grain yield per plant was assessed by weighing grains at maturity and correcting the value to 13 % moisture. Posteriorly, thirty-one sample scenarios were simulated per experimental unit, performing an analysis of variance and applying the Shapiro-Wilk test at 5 % error probability to the experimental unit errors in each pre-defined sampling scenario. Also, four methodologies for determining the maximum curvature point to define sample size were compared, which are the general, spline, perpendicular distance, and linear plateau methods. The 95 % confidence interval width of the W statistic had an exponential decreasing response for trials that either meet or violate the error normality assumption. Small sample sizes per experimental unit led to biased estimates of the test, where it was either under or overestimated. The perpendicular distance and linear plateau methods were the most adequate to define the sufficient sample size for the W statistic, and at least 17 plants per experimental unit should be used for estimating error normality for soybean grain yield reliably.

Functional imaging with dynamic quantitative oblique back-illumination microscopy.

.Significance: Quantitative oblique back-illumination microscopy (qOBM) is a recently developed label-free imaging technique that enables 3D quantitative phase imaging of thick scattering samples with epi-illumination. Here, we propose dynamic qOBM to achieve functional imaging based on subcellular dynamics, potentially indicative of metabolic activity. We show the potential utility of this novel technique by imaging adherent mesenchymal stromal cells (MSCs) grown in bioreactors, which can help address important unmet needs in cell manufacturing for therapeutics.Aim: We aim to develop dynamic qOBM and demonstrate its potential for functional imaging based on cellular and subcellular dynamics.Approach: To obtain functional images with dynamic qOBM, a sample is imaged over a period of time and its temporal signals are analyzed. The dynamic signals display an exponential frequency response that can be analyzed with phasor analysis. Functional images of the dynamic signatures are obtained by mapping the frequency dynamic response to phasor space and color-coding clustered signals.Results: Functional imaging with dynamic qOBM provides unique information related to subcellular activity. The functional qOBM images of MSCs not only improve conspicuity of cells in complex environments (e.g., porous micro-carriers) but also reveal two distinct cell populations with different dynamic behavior.Conclusions: In this work we present a label-free, fast, and scalable functional imaging approach to study and intuitively display cellular and subcellular dynamics. We further show the potential utility of this novel technique to help monitor adherent MSCs grown in bioreactors, which can help achieve quality-by-design of cell products, a significant unmet need in the field of cell therapeutics. This approach also has great potential for dynamic studies of other thick samples, such as organoids.

Rapid Growth of Large Forest Fires Drives the Exponential Response of Annual Forest-Fire Area to Aridity in the Western United States.

Annual forest area burned (AFAB) in the western United States (US) has increased as a positive exponential function of rising aridity in recent decades. This non‐linear response has important implications for AFAB in a changing climate, yet the cause of the exponential AFAB‐aridity relationship has not been given rigorous attention. We investigated the exponential AFAB‐aridity relationship in western US forests using a new 1984–2019 database of fire events and 2001–2020 satellite‐based records of daily fire growth. While forest‐fire frequency and duration grow linearly with aridity, the exponential AFAB‐aridity relationship results from the exponential growth rates of individual fires. Larger fires generally have more potential for growth due to more extensive firelines. Thus, forces that promote fire growth, such as aridification, have more potent effects on larger fires. As aridity increases linearly, the potential for growth of large fires accelerates, leading to exponential increases in AFAB.

Light utilisation, leaf canopy properties and fruiting responses of narrow-row, planar cordon apple orchard planting systems—A study of the productivity of apple

Growth (annual increase in total dry mass) and productivity of agricultural cropping systems depend on the fractional interception of total seasonal light energy. With single-row hedgerow intensive apple orchard systems the reported maximum light utilisation is ∼60% fractional interception. This limitation is determined by row layout and hedgerow tree architecture, constraining apple orchard productivity to maxima of 100–120 t ha−1. We hypothesised that reducing the orchard system between-row spacing to 1.5 m to 2.0 m would increase light interception and productivity, using two-dimensional, narrow planar hedgerow trees to maintain within-canopy irradiance. Trees comprised 10 vertical fruiting stems, equally spaced along 3 m of horizontal cordon (the planar cordon tree), requiring 2222 and 1667 trees per ha. By the seventh year the maximum fractional light interception (LI) was 77% in 1.5 m row plots and 66% in 2.0 m row plots, averaged across four cultivars (‘Gala’, ‘Scifresh’, ‘Scilate’, ‘Fuji’). The LAI-light interception response relationship of 6- and 7-year-old plots indicated a potential maximum light interception above 80% at a LAI of 3.5–5.0. Annual yields increased in proportion to increasing annual maximum fractional light interception from year two onwards and described an exponential response: Yield = 5.085e0.0483LI, (R2 = 0.88). The yield response to maximum fractional light interception exceeded the hypothesised 169 t ha−1 at 90% light interception in spindle systems proposed by Palmer et al. (2002). The yield–light utilisation behaviour of planar cordon systems indicate the productivity potential of apple as a crop has been under-estimated; a theoretical maximum annual yield of market-quality apples at ∼90% light utilisation (≥ 2000 MJ m−2 light energy per season) is estimated to be in a range of 250–315 t ha−1. Productivity amongst cultivars and regions will be influenced by the cultivar genetics for fruit size, relative maturation dates and production microclimates, because these orchard systems will function at the upper limit of light utilisation.

Magnetic all-inorganic perovskite nanocrystals demonstrating well-defined hybrid structure and superhydrophobic behavior towards movable and stable photoluminescence

As an emerging light-harvesting and light-emitting materials, all-inorganic perovskite nanocrystals (AIPNCs) have attracted widespread attentions in optoelectronic materials and devices. However, great challenges exist for the fabrication of novel AIPNCs demonstrating magnetic properties. Here, magnetic AIPNCs (M-AIPNC) with well-defined hybrid nanostructures of Fe3O4@SiO2@AIPNC are proposed. The core-shell structured chemical encapsulation and the in-situ crystal growth are designed to fabricate the magnetic substrate of Fe3O4@SiO2 and to link the AIPNCs, respectively. The as-prepared M-AIPNCs display brilliant blue, green and red photoluminescence by adjusting the chemical compositions of AIPNCs. The M-AIPNCs also demonstrate enhanced water stability due to their surficial hydrophobicity. The photoluminescence of Fe3O4@SiO2@AIPNC in the heating process exhibits a power exponential temperature response, and can be recovered in the cooling process. Most importantly, the M-AIPNCs can realize the mobile photoluminescence driven by a remote magnetic field. The M-AIPNCs with multi-colored fluorescence, excellent fluorescence stability, reversible temperature response and non-contact motion control provide a brand-new strategy to create the next generation of intelligent optical materials.

Kinetics of delignification and carbohydrate degradation during the ozone bleaching of low-consistency hardwood pulps

With rising concern for environmental protection, the use of ozone has been increasingly studied in the pulp and paper industry. Feasible models for efficient quality prediction and process control are in high industrial demand. In this study, the reaction kinetics of delignification and viscosity during the ozone bleaching process (OBP) of low-consistency hardwood pulp (LCHP) are explored using exponential and zero-order response models, respectively. The effects of ozone dose, reaction temperature, reaction time, and pH on the changes in residual lignin content and pulp viscosity were analyzed. The corresponding kinetic parameters, such as the reaction order, rate constant (k), and activation energy (E), were also obtained. The models suggest that temperature should be one of the most significant factors affecting the effectiveness of the OBP system. The strategy to improve the OBP selectivity is based on reducing the reaction temperature while increasing the ozone concentration and pH in the reaction system.