Complex Chemical Processes Research Articles

Direct Observation of Sulfate Explosive Growth in Wet Plumes Emitted From Typical Coal‐Fired Stationary Sources

AbstractThe origins of atmospheric sulfate production have previously been explained by focusing on air quality models and complex chemical reaction processes. Here, we first report direct observations of sulfate production in stack plumes discharged from coal‐fired power plants, industrial boilers, and sintering plants equipped with wet desulfurization systems. Less than one third of the particulate SO42− in plumes is attributed to dust‐SO42− and SO3 measured in stacks. The SO2 aqueous‐phase oxidation process is critical in explaining the unknown sulfate formation in plume droplets with pH values ranging from 2.3 to 2.8. When the rapidly formed sulfate in wet plumes is included, a notable amount of underestimated sulfate (∼0.24 Tg in 2017) is emitted from industrial stacks in China and can partially explain the “missing sulfate” on driving most particle pollution episodes. Policy‐making targeting particulate emissions is suggested to substantially reduce sulfate emissions for further air quality improvement.

Performance-based simulants for Hanford radioactive waste treatment process testing

ABSTRACT Radioactive wastes from nuclear fuel processing are stored in large underground storage tanks at the Hanford Nuclear Reservation. Treatment and remediation require that waste feed from the storage tanks be delivered to the Hanford Tank Waste Treatment and Immobilization Plant continually for the duration of the treatment mission. The complex physical and chemical processes required for this mission, the significant scale, and the hazardous nature of the waste necessitate the use of simulants in process testing. A new class of simulants, “performance-based simulants,” has been developed to match the process performance of the simulant to actual waste performance data.

An Improved Synthesis of N-(4-[18F]Fluorobenzoyl)-Interleukin-2 for the Preclinical PET Imaging of Tumour-Infiltrating T-cells in CT26 and MC38 Colon Cancer Models.

Positron emission tomography (PET) imaging of activated T-cells with N-(4-[18F]fluorobenzoyl)-interleukin-2 ([18F]FB-IL-2) may be a promising tool for patient management to aid in the assessment of clinical responses to immune therapeutics. Unfortunately, existing radiosynthetic methods are very low yielding due to complex and time-consuming chemical processes. Herein, we report an improved method for the synthesis of [18F]FB-IL-2, which reduces synthesis time and improves radiochemical yield. With this optimized approach, [18F]FB-IL-2 was prepared with a non-decay-corrected radiochemical yield of 3.8 ± 0.7% from [18F]fluoride, 3.8 times higher than previously reported methods. In vitro experiments showed that the radiotracer was stable with good radiochemical purity (>95%), confirmed its identity and showed preferential binding to activated mouse peripheral blood mononuclear cells. Dynamic PET imaging and ex vivo biodistribution studies in naïve Balb/c mice showed organ distribution and kinetics comparable to earlier published data on [18F]FB-IL-2. Significant improvements in the radiochemical manufacture of [18F]FB-IL-2 facilitates access to this promising PET imaging radiopharmaceutical, which may, in turn, provide useful insights into different tumour phenotypes and a greater understanding of the cellular nature and differential immune microenvironments that are critical to understand and develop new treatments for cancers.

Emerging hybrid biomaterials for oxidative stress induced photodynamic therapy.

Cancer therapy has undergone tremendous advancements in the past few years. The drawbacks of most of these therapies have encouraged researchers to obtain further insight into the complex chemical, biochemical and biological processes ongoing in the evolving cancer cells. These studies have led to an advent of reactive oxygen species mediated therapies to target and disrupt the cancer pathology. Photodynamic therapy (PDT) has emerged as a potent candidate for oxidative stress mediated non-invasive technique for rapid diagnosis and treatment of cancer. Towards this, biomacromolecules derived hybrid nanomaterials have contributed largely in the development of various therapeutics and theranostics for efficacious cancer management that can assist PDT. This review summarizes various hybrid biomaterials and advanced techniques that have been explored widely in the past few years for PDT application. The article also mentions some of the important in-vitro and in-vivo developments and observations explored by employing these materials for PDT application. The article also describes the interactions of these materials at the biological interface and the probable mechanism that assist in generation of oxidative stress and subsequent cell death.

Thermodynamic Theory of Silicon Chemical Vapor Deposition

Silicon chemical vapor deposition via silane is determined with classical thermodynamics. Drastic assumptions are used, such as the hydrogen carrier gas does not interact with the silicon thin film growth and the energy of reaction is adjusted to experiments. As a result, a linear function of temperature akin to a Gibbs free energy of reaction is shown to control silicon growth rates, and the characteristic Arrhenius plot is fully determined. The surface response of the growth rate activation energy is neatly mapped, bringing clarity to the parameter space for which a single value is a valid metric. A meta-analysis of growth rates is carried out for a demonstration of the portability of the linear function of temperature across reactors and to mine out reactor scaling factors. A silane heterogeneous decomposition reaction is also a great model chemistry for chlorosilanes. Silicon deposition is notoriously a complex chemical process, but with thermodynamics, the reaction mechanism is successfully reduced to the essentials.

Regression Methods Based on Nearest Neighbors with Adaptive Distance Metrics Applied to a Polymerization Process

Empirical models based on sampled data can be useful for complex chemical engineering processes such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. In this case, the goal is to predict the monomer conversion, the numerical average molecular weight and the gravimetrical average molecular weight. This process is characterized by non-linear gel and glass effects caused by the sharp increase in the viscosity as the reaction progresses. To increase accuracy, one needs more samples in the areas with higher variation and this is achieved with adaptive sampling. An extensive comparative study is performed between three regression algorithms for this chemical process. The first two are based on the concept of a large margin, typical of support vector machines, but used for regression, in conjunction with an instance-based method. The learning of problem-specific distance metrics can be performed by means of either an evolutionary algorithm or an approximate differential approach. Having a set of prototypes with different distance metrics is especially useful when a large number of instances should be handled. Another original regression method is based on the idea of denoising autoencoders, i.e., the prototype weights and positions are set in such a way as to minimize the mean square error on a slightly corrupted version of the training set, where the instances inputs are slightly changed with a small random quantity. Several combinations of parameters and ways of splitting the data into training and testing sets are used in order to assess the performance of the algorithms in different scenarios.

Вопросы преподавания математического анализа в медицинских вузах

The contradiction between the rapid mathematization of health care through the active introduction of modern technologies and methods based on mathematical achievements in the field of medicine and the lack of a system of training medical students corresponding to these scientific successes, which allows them to carry out mathematical modeling of complex physical, chemical and biological processes at the molecular level for the purpose of their analysis and subsequent forecasting, , problems that arise when teaching mathematical analysis in medical Schools, and possible ways to solve them. The solution identified during the research of problematic issues of teaching of mathematical analysis in medical schools is offered by enhancing the quality of learning through the re-allocated for the development of the discipline course hours stimulate the motivation of students, adjusting content and methodical component of teaching and active introduction in educational process of modern information technologies, elimination of excessive mathematical formalism.

Using Reinforcement Learning for Generating Polynomial Models to Explain Complex Data

Basic oxygen steel making is a complex chemical and physical industrial process that reduces a mix of pig iron and recycled scrap into low-carbon steel. Good understanding of the process and the ability to predict how it will evolve requires long operator experience, but this can be augmented with process target prediction systems. Such systems may use machine learning to learn a model of the process based on a long process history, and have an advantage in that they can make use of vastly more process parameters than operators can comprehend. While it has become less of a challenge to build such prediction systems using machine learning algorithms, actual production implementations are rare. The hidden reasoning of complex prediction model and lack of transparency prevents operator trust, even for models that show high accuracy predictions. To express model behaviour and thereby increasing transparency we develop a reinforcement learning (RL) based agent approach, which task is to generate short polynomials that can explain the model of the process from what it has learnt from process data. The RL agent is rewarded on how well it generates polynomials that can predict the process from a smaller subset of the process parameters. Agent training is done with the REINFORCE algorithm, which enables the sampling of multiple concurrently plausible polynomials. Having multiple polynomials, process developers can evaluate several alternative and plausible explanations, as observed in the historic process data. The presented approach gives both a trained generative model and a set of polynomials that can explain the process. The performance of the polynomials is as good as or better than more complex and less interpretable models. Further, the relative simplicity of the resulting polynomials allows good generalisation to fit new instances of data. The best of the resulting polynomials in our evaluation achieves a better R^2 score on the test set in comparison to the other machine learning models evaluated.

Computer Information System multi-criteria optimization conditions of the complex chemical processes

An information-computing system for multi-criteria optimization of complex chemical processes has been developed. Considered sensing algorithm for solving the problem of multicriteria optimization. Information and computing system combines subsystems for solving individual computing problems. Each subsystem is designed for separate calculations-direct kinetic task, calculation of the inverse kinetic problem, multi-criteria optimization of the reaction conditions. For the catalytic reaction of the benzylalkyl ethers synthesis, using the developed information-computational system, the problem of optimizing the conditions has been solved. Based on the kinetic model, the optimal values of temperature and reaction time are determined. The values of the parameters for assessing the quality of the Pareto approximation are calculated.

Fault Diagnosis of Complex Chemical Processes Using Feature Fusion of a Convolutional Network

Chemical production usually shows complex, higher-dimensional, time-varying, and non-Gaussian characteristics, which make it difficult to judge the normal operation of the states of chemical processes. The various and similar fault states in chemical processes cause difficulties to existing fault diagnosis methods. In order to solve this problem, a feature fusion fault diagnosis method using a normalized convolutional neural network for complex chemical processes is proposed in this paper, which consists of the depth-normalized convolution network, improved second-order pooling, and multilayer perceptron. First, the original data that have been simply processed are put into the depth-normalized convolution network to extract features of the faulty states. Second, the output of depth convolution modules enters into the improved second-order pooling to proceed feature fusion for further refining the state characteristics. Finally, the multilayer perceptron is used to extract and compress the following features, and the final diagnosis results are obtained. In this paper, the normalized convolution network model is established by adding the batch normalization into a one-dimensional convolution network, where the problems of gradient explosion and disappearance are partly avoided. Meanwhile, the convergence speed of the network is also accelerated. The experimental tests on a Tennessee Eastman process and a coking furnace process show that the proposed method is more advanced than existing deep learning fault diagnosis methods.

A national wide evaluation of heavy metals pollution in surface sediments from different marginal seas along China Mainland

Sediments, both act as sink and source of heavy metals, played an important role in their global cycle. In coastal ecosystems, heavy metals will be deposited and accumulated in sediments via complex chemical, physical and biological processes and pose potential risks to ecosystems. In the past 3 decades, studies have reported the heavy metal contamination in different ecosystems along China Mainland on local or regional scales, and their concentrations, distribution patterns, sources, as well as ecological risks showed large spatial variabilities. Here, the concentrations of heavy metals in different coastal ecosystems along China Mainland were reviewed from more than 150 published papers. Their distributions patterns, sources and the ecological risks were investigated. As a result, Cr, Cu, Ni, Zn and Pb were the most reported heavy metals with high concentrations observed in nearshore estuaries and coastal bays, which is closely related to the intensive human activities in nearshore areas. Whereas, low heavy metal concentrations were observed in offshore sediments. Source identification suggested that the heavy metals in coastal ecosystems were mainly from phosphate fertilization, atmospheric deposition, weathering and industrial activities. Results from ecological risk assessment indicated that there were high ecological risks in some pollution hot-spots, associated with their high As, Cd, Zn and Pb concentrations.

Experimental Study on Physical and Mechanical Properties of Gypsum Rock During High-Temperature Dehydration–Hydration Expansion

In order to study the physical and mechanical properties of gypsum rock samples in three states (natural, high-temperature dehydration and hydration time), natural gypsum rock was put into high-temperature condition of 220°C for dehydration treatment, and then the high-temperature dehydrated gypsum rock was treated with different hydration time, and the gypsum rock samples in three states were subjected to ultrasonic test, density test and uniaxial compression test. The results show that the main components of natural gypsum minerals were gypsum dihydrate (71%), dolomite (27%) and potassium chloride (2%). The hydration of high-temperature dehydration samples was an extremely complex physical and chemical process. Hydration had a significant strengthening effect on gypsum rock, and the weak surface of the structure had a significant weakening effect on it during the hydration process. As hydration time increased, the apparent density increased gradually and the longitudinal wave velocity increased. The peak strength of the sample decreased first and then increased, and it generally had a logarithmic relationship with hydration time. The peak strain decreased first and then increased, then decreased and fluctuated, and the elastic modulus first increased and then decreased and then increased again. The expansion rate and limited expansion force of the sample increased with increase in hydration time. After a certain hydration time, the expansion rate of the sample tended to be stable, while the limited expansion force began to decrease slowly after reaching the maximum value.

Enhancing Science Education Through Visual Chemistry

Chemical scientists are vital to numerous important areas of society – from pharmaceutical drug development to solar energy conversion. To prepare for their future careers, it is critical for chemistry students to gain a deep understanding of complex molecular processes. However, chemistry is full of abstract concepts that can be difficult to grasp, causing many students to fall behind. To address this issue, Dr Marcos Caroli Rezende and Dr Carolina Aliaga Vidal at Universidad de Santiago de Chile have been developing visual learning methods to improve the quality of chemistry education, and ensure better outcomes for all students. In their recent work, they have focused on helping students to visualise a complex chemical process known as phase-transfer catalysis.

Animal and Plant Protein Oxidation: Chemical and Functional Property Significance.

Protein oxidation, a phenomenon that was not well recognized previously but now better understood, is a complex chemical process occurring ubiquitously in food systems and can be induced by processing treatments as well. While early research concentrated on muscle protein oxidation, later investigations included plant, milk, and egg proteins. The process of protein oxidation involves both radicals and nonradicals, and amino acid side chain groups are usually the site of initial oxidant attack which generates protein carbonyls, disulfide, dityrosine, and protein radicals. The ensuing alteration of protein conformational structures and formation of protein polymers and aggregates can result in significant changes in solubility and functionality, such as gelation, emulsification, foaming, and water-holding. Oxidant dose-dependent effects have been widely reported, i.e., mild-to-moderate oxidation may enhance the functionality while strong oxidation leads to insolubilization and functionality losses. Therefore, controlling the extent of protein oxidation in both animal and plant protein foods through oxidative and antioxidative strategies has been of wide interest in model system as well in in situ studies. This review presents a historical perspective of food protein oxidation research and provides an inclusive discussion of the impact of chemical and enzymatic oxidation on functional properties of meat, legume, cereal, dairy, and egg proteins based on the literature reports published in recent decades.

Effective algorithm for simulations of layer-by-layer growth during pulsed-laser deposition.

The atomistic simulation of materials growing in the layer-by-layer mode by the pulsed-laser deposition is a significant challenge mainly due to the short timescales in which the fastest processes on the surface occur together with long periods between pulses. We present a kinetic Monte Carlo algorithm which overcomes the scaling problem by approximation of fast diffusion and by neglecting complex chemical processes. The atomic diffusion is modeled as a two-dimensional gas of material units on each layer. The model is based on a few elementary processes-the condensation of units on the surface, their dissolution back to the gas, and interlayer transport, which can be influenced by the Ehrlich-Schwoebel barrier. With these simplifications, the computational time of the algorithm scales only linearly with the size of the substrate while describing physically relevant growth kinetics. We demonstrate that the simplified model is suitable for simulations of layered growth of thin films in the range from quasicontinuous deposition to low-frequency cases. The model is successfully implemented to provide an alternative explanation of the time evolution of layer coverages by interlayer transport after pulses of deposition experimentally observed during perovskite growth [G. Eres et al., Phys. Rev. B 84, 195467 (2011)PRBMDO1098-012110.1103/PhysRevB.84.195467].

Study on the effect of organic sulfur on coal spontaneous combustion based on model compounds

The composition and structure of organic sulfur in coal are rather complicated. Thus, no workable method has been adopted to accurately and quantitatively study organic sulfur in coal so far. Coal spontaneous combustion (CSC) is a severe disaster occurring widely in coal industry. It is an extremely complex physical and chemical process as well as a major scientific and technical problem that has always puzzled researchers. Studying the complex chemical reaction of coal by model compounds is a reasonable method extensively recognized and used in the international coal chemistry research field. Model compounds represent certain kinds of specific functional groups in coal. Through an investigation on the reaction mechanism of model compounds, the complex mechanism of coal molecules can be revealed. In this study, in order to better investigate the mechanism of organic sulfur during CSC, representative organic sulfur model compounds containing different organic sulfur functional groups were selected for the low-temperature oxidation experiment and the thermal analysis. Their oxygen consumption amounts and oxidized products concentrations were determined by gas chromatography and infrared analyzer. Besides, the weights and endothermic/exothermic rates of these model compounds during low-temperature oxidation were measured using the thermogravimetry/differential scanning calorimetry (TG/DSC) method. In this way, their oxidative and thermodynamic characteristics were revealed. On this basis, the organic sulfur model compounds were uniformly mixed with the coal samples for the low-temperature oxidation experiment in the hope of exploring its effect on oxygen consumption amounts and indicative gas products during CSC. Finally, the mechanism of the interactions between organic sulfur and other reactive groups in coal during low-temperature oxidization were obtained.

Nitrogen-doped nanoporous graphene induced by a multiple confinement strategy for membrane separation of rare earth.

SummaryRare earth separation is still a major challenge in membrane science. Nitrogen-doped nanoporous graphene (NDNG) is a promising material for membrane separation, but it has not yet been tested for rare earth separation, and it is limited by multi-complex synthesis. Herein, we developed a one-step, facile, and scalable approach to synthesize NDNG with tunable pore size and controlled nitrogen content using confinement combustion. Nanoporous hydrotalcite from Zn(NO3)2 is formed between layers of graphene oxide (GO) absorbed with phenylalanine via confinement growth, thus preparing the sandwich hydrotalcite/phenylalanine/GO composites. Subsequently, area-confinement combustion of hydrotalcite nanopores is used to etch graphene nanopores, and the hydrotalcite interlayer as a closed flat nanoreactor induces two-dimensional space confinement doping of planar nitrogen into graphene. The membrane prepared by NDNG achieves separation of Sc3+ from the other rare earth ions with excellent selectivity (∼3.7) through selective electrostatic interactions of pyrrolic-N, and separation selectivity of ∼1.7 for Tm3+/Sm3+.

The morphology evolution of char particles during conversion processes

The change in a particle’s morphology affects both its trajectory and its carbon consumption rate. For that reason, the present work studies the evolution of particle’s morphology during the char conversion process. Particle-resolved transient CFD calculations were performed to estimate changes in particle density and volume. The complex physical and chemical processes taking place in the vicinity and the interior of the char particle were studied and discussed in detail for different process conditions and reaction regimes. The char conversion study confirmed the theoretical correlation between the char diameter and the char density exponent. New expressions were discovered for both exponents α and β, depending on the effectiveness factor η and the stage of carbon conversion XC. In addition, the Random Pore Model, which describes the development of the specific surface area of the particle, was extended to include an empirical correlation γ, making it valid for different reaction regimes and operating conditions.

Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model

Improving production efficiency and optimizing resources can accelerate the sustained and stable development of complex chemical processes. This paper presents novel energy analysis and resource optimization model based on data envelopment analysis cross-model integrated interpretative structural model and analytic hierarchy process to effectively simplify input indicators. The interpretative structural model can divide production data with many uncertain dimensions into some sub-elements with obvious hierarchical relationships. Then the sub-elements in the same layer are merged into a major element by the analytic hierarchy process method for further simplifying input indicators of the improved DEACM. At last, the proposed method is used to build the energy analysis and resource optimization model for PTA and ethylene production systems in complex chemical processes. In our experiments, the difference in efficiency values obtained by the proposed model is more significant and accurate. In addition, it can optimize production efficiency and guide ineffective production processes. The electrical conductivity of the purified terephthalic acid systems can reduce by 0.47%. And the ethylene production of the ethylene production systems can increase by 3.93%, and the carbon emissions of the ethylene production system can reduce by 25,297 tons approximately.

Fluids in Geothermal Systems

Hot fluids are nearly ubiquitous in volcanic environments in the Earth’s crust. Magma at depth heats groundwater which then ascends towards the Earth’s surface through faults, fractures, and otherwise permeable rocks. Fluids in geothermal systems offer direct insight into the many complex chemical and physical processes that occur in these extreme environments. They are also analogues of many ore-forming systems. Scientists have advanced our understanding of fluids in geothermal systems by studying wells sunk ~2–3 km deep into many geothermal fields. Today, we are targeting deeper and hotter reservoirs, at or near the contact of magmatic bodies, which provide unique opportunities to study, and potentially utilize, super-critical fluid resources in the near future.