Complex Chemical System Research Articles

Agent-based modeling methodology and temporal simulation for Natech events in chemical clusters

In chemical clusters, technological accidents triggered by natural events (Natech events) can lead to the failure of large number of units and cause catastrophic consequences. Previous studies on Natech events give primary concerns on the static analysis at small-scale region with few thoughts the impact of various safety barriers. From the perspective of the large-scale region and temporal characteristics, an agent-based modeling methodology is proposed to present the dynamic evolution process. All the units associated with the evolution are divided into natural hazard agent, hazard-affected facility agent, safety barrier agent, and escalation factor agent to reduce the computational complexity. The time-dependent state transition mechanism of each agent is analyzed to represent the evolution process, simultaneously, the temporal effect of natural hazards, safety barrier and multi escalation factors is considered by utilization of time steps. Moreover, Monte Carlo simulation is adopted to quantify the uncertainties accompanying an accident evolution. The proposed methodology is demonstrated on a complex chemical system in the large-scale region. The results of simulation show that the number of failed hazard-affected facilities in the Natech events scenario will cyclically increase and keep constant, with strong temporal characteristic. The intensity of natural hazards and the probability of failure of safety barriers make a big impact on the temporal characteristic. The results are valuable to support natural hazard prevention in chemical cluster.

The anti-hepatocellular carcinoma effect of Aidi injection was related to the synergistic action of cantharidin, formononetin, and isofraxidin through BIRC5, FEN1, and EGFR

Ethnopharmacological relevanceAidi injection (ADI) is a popular anti-tumor Chinese patent medicine, widely used in clinics for the treatment of hepatocellular carcinoma (HCC) with remarkable therapeutic effects through multiple targets and pathways. However, the scientific evidence of the synergistic role of the complex chemical component system and the potential mechanism for treating diseases are ignored and remain to be elucidated. Aim of the studyThis study aimed to elucidate and verify the cooperative association between the potential active ingredient of ADI, which is of significance to enlarge our understanding of its anti-HCC molecular mechanisms. Materials and methodsFirstly, the anti-HCC effect of ADI was evaluated in various HCC cells and the zebrafish xenograft model. Subsequently, a variety of bioinformatic technologies, including network pharmacology, weighted gene co-expression network analysis (WGCNA), meta-analysis of gene expression profiles, and pathway enrichment analysis were performed to construct the competitive endogenous RNA (ceRNA) network of ADI intervention in HCC and to establish the relationship between the critical targets/pathways and the key corresponding components, which were involved in ADI against HCC in a synergistic way and were validated by molecular biology experiments. ResultsADI exerted remarkable anti-HCC in vitro cells and in vivo zebrafish model, especially that the Hep 3B2.1-7 cell showed substantial sensibility to ADI. The ceRNA network revealed that the EGFR/PI3K/AKT signaling pathway was identified as the promising pathway. Furthermore, the meta-analysis also demonstrated the critical role of BIRC5 and FEN1 as key targets. Finally, the synergistic effect of ADI was revealed by discovering the inhibitory effect of cantharidin on BIRC5, formononetin on FEN1 and EGFR, as well as isofraxidin on EGFR. ConclusionOur study unveiled that the incredible protective effect of ADI on HCC resulted from the synergistic inhibition effect of cantharidin, formononetin, and isofraxidin on multiple targets/pathways, including BIRC5, FEN1, and EGFR/PI3K/AKT, respectively, providing a scientific interpretation of ADI against HCC and a typical example of pharmacodynamic evaluation of other proprietary Chinese patent medicine.

COMPONENT COMPOSITION OF INKS, PAINTS AND DYES AS AN IMPORTANT COMPONENT OF DOCUMENT PROTECTION AGAINST COUNTERFEITING

This article analyzes the component composition of inks, paints and dyes, and also gives a general description of one or another type of ink or dye and a classification of some of them. The historical aspect of the manufacture of inks and their components is highlighted. Writing ink was first made in ancient Egypt and China around 2500 BC. The features of paints depending on the type of printing (high, gravure, flat offset, screen) are studied and examples of their application are given. Flat offset inks used in newspapers are usually simple in composition. As for letterpress, although this type is gradually being replaced by other printing processes, it is still used in the production of money, most paper-based forms in printing serial numbers, etc. Gravure inks are similar to flexographic inks, except that ketones and aromatic hydrocarbons can be used as solvents, allowing much greater freedom in the choice of binders. This type of ink for inkjet printing, such as solvent ink, is considered in detail. Solvent ink is a complex physical and chemical system for inkjet printing, consisting of pigment, polymer, film former, solvent and special additives. The structural composition of solvents is characterized, which, getting on paper, undergoes a number of changes over a fixed period of time, as a result of which the dye dries on paper. Ethanol, benzyl alcohol and many other solvents can also be used as carriers. The choice of solvent or solvents often depends on the properties of the writing instrument. The so-called «invisible» ink (UV ink, thermosetting ink, etc.) was studied separately as a kind of protective element. This type of ink is mainly used in steganography, anti-counterfeiting and currency security features, packaging authenticity indicators, brand protection, and secret messages. Conclusions are drawn, emphasizing that understanding the principle of operation of one or another element of protection allows forensic experts of forensic technical examination of documents to effectively identify forgery.

Integrating process dynamics in data-driven models of chemical processing systems

Data-driven models require high-fidelity data of sufficient quantity and granularity. This is challenging in a complex chemical processing system due to frequent sensor breakdown, process shutdown, malfunctioning of equipment, random fluctuations, miscalibration, inconsistent sampling frequencies, and data entry errors. Thus many models scoring well on training data flounder on the real-time data of industrial systems. This work presents a process dynamics-guided neural network (PDNN) model to improve model generalization that can maintain higher performance in sparse and low-quality data. This has been enacted by adding an additional layer in the neural network architecture to incorporate process dynamics such as material and energy balance equations, universal laws, standard correlations, and field knowledge. We evaluated the proposed model against a standard neural network on a regression and a classification tasks representing a steady state and transient behavior of processing systems. The proposed model yielded improved outcomes on reduced sample-sized data and in extrapolated regimes implying a higher generalization capability of the PDNN model. The proposed process dynamics-guided neural network can be employed as a robust model for handling generalization issues of data-driven methods in processing systems.

Monitoring Chemical Accidents in Industrial Complexes Using Tower-Installed Infrared System for Remote Chemical Detection and Long-Range Video Surveillance System

Chemical industrial complexes are extensive, complex structures with large-scale chemical facilities where large quantities of various chemical substances are handled. Detection equipment must be installed in high locations to monitor these industrial complexes and detect chemical accidents from a distance. In previous studies, individual monitoring equipment was temporarily installed on the ground, on a rooftop, or on a vehicle to detect chemical accidents from a distance. In this study, however, the industrial complex chemical accident monitoring system was developed by combining different technologies and was installed on a tower. For the Yeosu National Industrial Complex (which functioned as a test bed), 70m-high steel towers were built. Additionally, an infrared system for remote chemical detection (SIGIS-2, Bruker) and a long-range video surveillance system (TORUSS-LR2000, Globalsystems) were installed at the top of steel towers to monitor the entire industrial complex. The target substances to be monitored in real time by the infrared system for remote chemical detection were selected, and the monitoring sections were classified to enable each piece of equipment to distinguish the scanned areas. To improve the accuracy of the detection results, the information about the actual handled substances and respective facilities of the sites in the industrial complex was inserted into the database of the system and then connected to the sections. During the three-month test operation, various chemical substances (including 1,3-butadiene, methanol, methylamine, ethyl acetate, ammonia, and vinyl chloride) were detected at each section in 20,034 cases, and the detection results were consistent with the inserted actual information. The accumulated detection data shows that the detection frequency of a specific chemical substance was high in each section. This can be used as a basis for modifying the threshold of the anomaly detection model, thereby improving the accuracy of the system. Therefore, this system can detect and evaluate the leakage of chemical substances and the occurrence of fires or smoke through large-scale scans 24 h per day. Furthermore, it can be used for the early detection of and effective responses to chemical accidents in industrial complexes.

Real-time, in situ measurement of H2O generated during in situ combustion tests using 1f-normalized wavelength modulation spectroscopy with second harmonic detection.

The development and deployment of a real-time, in situ, non-invasive sensor to monitor the concentration of H 2 O during in situ combustion (ISC) experiments with a heavy-crude oil is described. A real-time sensor to monitor the gas-phase products from ISC can support the study of the kinetics of the complex chemical reactive system in ISC. The mole fraction of H 2 O was measured using tunable diode laser (TDL) absorption spectroscopy coupled with 1f-normalized wavelength modulation spectroscopy (WMS) and 2f detection. The WMS 2f/1f strategy was used to enhance sensitivity with effective noise rejection, particularly suitable when characterizing the water vapor evolved from oil-water emulsions. H 2 O was measured at 3934.10c m -1 from the fundamental band v 3. That transition was selected using the HITRAN database to increase the line strength and minimize interference from neighbor compounds. Measurements of H 2 O concentration were conducted at ambient temperature and pressure using a reference cell (H 2 O=2% at 98.6kPa) to validate the sensor architecture under controlled laboratory environments. The TDL sensor was also successfully validated during real ISC experiments involving heavy-crude oil. Validation and combustion experiments showed the potential of the TDL-based sensor for non-invasive, real-time, in situ measurements of gas-phase species in conditions similar to those of laboratory-scale experimental ISC tests.

Multiple Gene Expression in Cell-Free Protein Synthesis Systems for Reconstructing Bacteriophages and Metabolic Pathways.

As a fast and reliable technology with applications in diverse biological studies, cell-free protein synthesis has become popular in recent decades. The cell-free protein synthesis system can be considered a complex chemical reaction system that is also open to exogenous manipulation, including that which could otherwise potentially harm the cell's viability. On the other hand, since the technology depends on the cell lysates by which genetic information is transformed into active proteins, the whole system resembles the cell to some extent. These features make cell-free protein synthesis a valuable addition to synthetic biology technologies, expediting the design-build-test-learn cycle of synthetic biology routines. While the system has traditionally been used to synthesize one protein product from one gene addition, recent studies have employed multiple gene products in order to, for example, develop novel bacteriophages, viral particles, or synthetic metabolisms. Thus, we would like to review recent advancements in applying cell-free protein synthesis technology to synthetic biology, with an emphasis on multiple gene expressions.

Dynamic Community Detection Decouples Multiple Time Scale Behavior of Complex Chemical Systems.

Although community or cluster identification is becoming a standard tool within the simulation community, traditional algorithms are challenging to adapt to time-dependent data. Here, we introduce temporal community identification using the Δ-screening algorithm, which has the flexibility to account for varying community compositions, merging and splitting behaviors within dynamically evolving chemical networks. When applied to a complex chemical system whose varying chemical environments cause multiple time scale behavior, Δ-screening is able to resolve the multiple time scales of temporal communities. This computationally efficient algorithm is easily adapted to a wide range of dynamic chemical systems; flexibility in implementation allows the user to increase or decrease the resolution of temporal features by controlling parameters associated with community composition and fluctuations therein.

Нейросетевой метод анализа процессов термической обработки окомкованного фосфатного рудного сырья

Currently, there is an acute problem of waste disposal of mining and processing plants, which accumulate in significant volumes in the territories adjacent to them and pose a serious threat to the environment. In this regard, the creation of technological systems for processing ore waste and the improvement of their information support represent an urgent area of research. An example of such a system is a complex chemical and energy technology system for the production of yellow phosphorus from waste apatite-nepheline ores. The purpose of the study was to develop a model for collecting data on the parameters of the processes of heat treatment of pelletized phosphate ore raw materials in such a system, as well as a method for identifying dependencies between these parameters. The identification of dependencies in the information support of the yellow phosphorus production system will improve the quality of its functioning in terms of management criteria, energy and resource efficiency. To achieve this goal, the tasks of choosing a mathematical concept for the basis of the method being developed, constructing an algorithm and creating software implementing this method, conducting model experiments were solved. The method is based on the use of deep recurrent neural networks of long-term short-term memory, which have a high generalizing ability and are used in solving problems of regression and classification of multidimensional time sequences, in the form of which, as a rule, the parameters of a chemical and energy technology system are presented. The method is implemented as an application created in the MatLab 2021 environment. The application interface allows you to interactively conduct experiments with various sets of input and output parameters to identify the relationship between them, as well as change the hyperparameters of neural networks. As a result of the application, a repository of trained neural networks is created that simulate the relationships found between the specified parameters of the technological system and can be applied in decision support systems, management and engineering.

Quality evaluation of the classical prescription, Danggui Jianzhong decoction, using ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology.

Danggui Jianzhong decoction is a classical prescription that has been widely used for thousands of years. However, the quality of this formula is difficult to control owing to its complex chemical component system. In this study, a simple and efficient method comprising ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology was established to evaluate the quality of this decoction. A total of 20 common peaks were obtained by fingerprint analysis and 19 chemicals were identified. The fingerprint similarity of 15 batch samples ranged from 0.963 to 0.991. Chemical pattern recognition analysis could clearly classify 15 batches of Danggui Jianzhong decoction into three groups. Further, seven chemical markers were screened out. A herbs-active components-targets-disease network was constructed and enrichment analyses were performed, which indicated that these 19 chemical components are the medicinal substances of Danggui Jianzhong decoction. Further, the mechanism employed by this formula to treat primary dysmenorrhea may be related to the regulation of inflammatory response. In conclusion, this combination approach enables accurate evaluation and prediction of the quality of Danggui Jianzhong decoction, and lays the foundation for studies on the material basis and exploration of the mechanism of Danggui Jianzhong decoction in the treatment of primary dysmenorrhea.

Accelerating complex chemical equilibrium calculations — A review

Incorporating multicomponent, multiphase, high-temperature, complex chemical equilibrium calculations into process and multiphysics models can provide significant insights into materials, processes and equipment. We refer to applications where the inclusion of these calculations provided insights that would otherwise be difficult to obtain. From these examples, the advantages and importance of including complex equilibria into models are clear for cases where more accurate descriptions of practically relevant systems are needed.Equilibrium calculations are, in general, omitted or incorporated in a simplified manner due to their computational expense. The equilibrium state of a complex chemical system is determined by minimising the Gibbs free energy for a given set of system component concentrations, temperature, and pressure. This minimisation routine is computationally expensive and makes direct integration of chemical equilibrium calculations into models infeasible.There have been many attempts to, in one way or another, accelerate these calculations. This includes methods such as creating look-up tables prior to the simulation or in-situ, fitting piecewise polynomial functions to thermochemical properties, phase diagram discretisation, sensitivity derivatives, machine-learning algorithms, and parallelisation. Pre-calculated databases tend to become very large and require much storage space, even when unstructured grids are used or piece-wise polynomials fitted. Neural network results do not adhere to physical laws such as mass conservation and large training sets are required to reduce this error.In-situ or on-demand methods of creating a database shows great promise because only the thermochemical regions that are of interest to the model are captured in the database, reducing the storage size and the amount of data to search through. No prior knowledge of the system is required to create the database.The Gibbs phase rule can be used to determine which geometrical features of a phase diagram to discretise and create a sparse database that covers large temperature, pressure and compositional ranges. The lever rule can then be used for fast and accurate interpolation between data points. Established thermochemical theory provides security for the decisions made within the discretisation and interpolation algorithms. Based on this review, an in-situ phase diagram discretisation method strongly based on thermochemical theory such as the Gibbs phase rule and the lever rule holds potential for significant acceleration of complex chemical equilibrium calculations.

An active learning approach for improving the performance of equilibrium based chemical simulations

In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach called active learning. The underlying idea of our approach is to consider the function to estimate as a sample of a Gaussian process which allows us to compute the global uncertainty on the function estimation. Thanks to this estimation and with almost no parameter to tune, the proposed method sequentially chooses the most relevant input data at which the function to estimate has to be evaluated to build a surrogate model. Hence, the number of evaluations of the function to estimate is dramatically limited. Our active learning method is validated through numerical experiments and applied to a complex chemical system commonly used in geoscience.

Алгоритм прогнозирования параметров системы переработки отходов апатит-нефелиновых руд

Within the framework of the concept of a circular economy, research in the field of creating technological systems for recycling waste from mining and processing plants occupies one of the key positions. This is connected, on the one hand, with significant volumes of such waste, reaching tens of millions of tons and posing a significant environmental hazard to air and water basins, human health, and, on the other hand, with their rich chemical and mineralogical composition, which makes it possible to call them accumulations of technogenic deposits. In this regard, the task of creating control systems for technological processes of processing such waste and their information support, including support for all stages of the passage of information processes, is urgent. The novelty of the presented research lies in the proposed structure of an intelligent control system for a complex chemical and energy technological system for processing apatite-nepheline ores, as well as in an algorithm for predicting technological parameters, which is part of the information support of the control system under consideration. The algorithm is based on the use of the apparatus of deep recurrent neural networks and Kalman filtering, which is used at the stage of data preprocessing to train the neural network. The paper describes the proposed algorithm for predicting multidimensional time series, adapted to the considered technological process, presents the software executed in the MatLab environment to demonstrate the efficiency of the specified combination of methods for processing technological parameters. In a model experiment, it has been shown that the use of filtering makes it possible to increase the accuracy of the forecast, which is especially noticeable at its large horizons. The practical significance of the research results is the proposed structure of an intelligent control system for the processing of apatite-nepheline ore waste and software for predicting its parameters, which can be used in various decision support systems.

New Software Application and Case Study That Simplify Teaching Complex Chemical Solubility and Equilibria

Chemical solubility and equilibria are paramount for understanding everyday systems. Recent events have led to more interest by the general public in chemical equilibria that occur in drinking water systems. This presents a great opportunity to increase student interest and engagement in the more complicated aspects of chemical equilibria. Shiny Apps allow exploration of complex equilibria without requiring that students (or instructors) get buried in the minutia. The application presented here, https://bazilio.shinyapps.io/LeadSolubilityCaseStudy/, can be used for instruction in chemistry or environmental science and only uses an internet browser such as Safari or Chrome; it is compatible with mobile browsers. Students are assigned a case study on the Flint, Michigan Water Crisis which guides them through lead sulfate, chloride, phosphate, and hydroxide equilibria, using the app to explore their intuitions about this complex chemical system.

Elucidating complex triplet-state dynamics in the model system isopropylthioxanthone

SummaryWe introduce techniques for probing the dynamics of triplet states. We employ these tools, along with conventional techniques, to develop a detailed understanding of a complex chemical system: a negative-tone, radical photoresist for multiphoton absorption polymerization in which isopropylthioxanthone (ITX) is the photoinitiator. This work reveals that the same color of light used for the 2-photon excitation of ITX, leading to population of the triplet manifold through intersystem crossing, also depletes this triplet population via linear absorption followed by reverse intersystem crossing (RISC). Using spectroscopic tools and kinetic modeling, we identify the reactive triplet state and a non-reactive reservoir triplet state. We present compelling evidence that the deactivation channel involves RISC from an excited triplet state to a highly vibrationally excited level of the electronic ground state. The work described here offers the enticing possibility of understanding, and ultimately controlling, the photochemistry and photophysics of a broad range of triplet processes.

Exposure of maize bioagrocenoses to diseases at no-till

The yield and quality of corn grain is significantly affected by the infection of plants with diseases of agricultural crops. The susceptibility of corn bioagrocenoses of hybrids ROSS 145 MV, ROSS 140 SV, Povolzhsky 107 SV, Katerina SV, NC Falcon, NC Gitago and Delitop to diseases during their cultivation for grain according to traditional, minimal and no-till technology in the agro-climatic conditions of the Volga-Vyatka region was studied. In the course of research, it was found that the most common diseases of corn due to excessive moisture in the soil and high humidity during the harvest period are mildew on the cob, fusarium on the cob, root and stem rot, dust and blister smut. Under unfavorable harvesting conditions, the incidence can reach 40 %. Under favorable weather conditions, there are isolated cases of plant diseases, regardless of the cultivation technology. To protect the bioagrocenoses of corn, it is necessary to grow disease-resistant hybrids, monitor the rotation of crops in the crop rotation, apply full doses of mineral fertilizers and micronutrients, sow at the optimum time with an appropriate seeding depth, maintain a complex chemical plant protection system, starting with seed dressing. The closest to the requirements are zoned maize hybrids with a short growing season ROSS 145 MV, ROSS 140 SV, Povolzhsky 107 SV and Katerina SV.

Influence of accelerator energy parameters and silicon carbide microparticle sizes on the changes in magnetic field induction during their acceleration

This article contains the microparticle accelerator scheme, the methods, and the results of practical study of magnetic field induction and electromagnetic radiation formed during explosion product ionization and energy accumulation during explosive charge detonation, as well as the influence of some process parameters on its change. The purpose of this work is to study the influence of accelerator energy parameters and silicon carbide microparticle sizes on the change in magnetic field induction during their acceleration. The influence of technological parameters on the electrodynamic properties of the ionization process of a complex chemical system, which is condensed EXPLOSIVES, was studied by the developed method based on the Hall effect with the use of the developed semiconductor Hall sensors and a special measuring complex. The average magnetic field induction value is 48 MT. The influence of the energy parameters of the accelerator (explosive charge mass), as well as of the size of microparticles introduced into the explosion products (PV) on the electrodynamic properties of the processes of ionization and acceleration of microparticles was determined by measuring and calculating magnetic field induction. Practical results were obtained and confirmed the particle size influence on the plasma state. With an increase in the particle size from 20 to 100 microns, the induction value increases to 50 MT and decreases sharply with a change in the size from 150 to 300 microns. The obtained dependences are the technological characteristics of the process of processing materials by high-speed flows of microparticles with the use of explosion energy, which can be adjusted to make the process manageable.

A Reformulation of Degree of Disequilibrium Analysis for Automatic Selection of Kinetic Constraints in the Rate-Controlled Constrained-Equilibrium Method

Abstract The rate-controlled constrained equilibrium (RCCE) is a model reduction scheme for chemical kinetics. It describes the evolution of a complex chemical system with acceptable accuracy with a number of rate controlling constraints on the associated constrained-equilibrium states of the system, much lower than the number of species in the underlying detailed kinetic model (DKM). Successful approximation of the constrained-equilibrium states requires accurate identification of the constraints. One promising procedure is the fully automatable Approximate Singular Value Decomposition of the Actual Degrees of Disequilibrium (ASVDADD) method that is capable of identifying the best constraints for a given range of thermodynamic conditions and a required level of approximation. ASVDADD is based on simple algebraic analysis of the results of the underlying DKM simulation and is focused on the behavior of the degrees of disequilibrium (DoD) of the individual chemical reactions. In this paper, we introduce an alternative ASVDADD algorithm. Unlike the original ASVDADD algorithm that require the direct computation of the DKM-derived DoDs and the identification of the set of linearly independent reactions, in the alternative algorithm, the components of the overall degree of disequilibrium vector can be computed directly by casting the DKM as an RCCE simulation considering a set of linearly independent constraints equaling the number of chemical species in size. The effectiveness and robustness of the derived constraints from the alternative procedure is examined in hydrogen/oxygen and methane/oxygen ignition delay simulations and the results are compared with those obtained from DKM.

Computer-Assisted Decision-Making System of Optimal Control over the Energy and Resource Efficiency of a Chemical Energotechnological System for Processing Apatite–Nepheline Ore Wastes

Some multilevel decision-making algorithms have been developed to provide optimal control over the energy and resource efficiency and environmental safety of a chemical energotechnological system designed for processing apatite–nepheline ore wastes and composed of a grinder, a granulator, a roasting conveyor machine, and an electric ore-smelting furnace (EOSF). The mutually dependent set of parameters of wet granules at the outlet from the tray granulator, roasted granules at the outlet from the roasting conveyor machine, and granules loaded into the EOSF and the characteristics of initial finely dispersed technogenic raw materials is taken into account alongside the effect of these waste-processing indices on the quality and purity of the finished product, namely, yellow phosphorus. Descriptive and mathematical formulations are given for the problem of optimal control over the energy and resource efficiency of a complex multistage chemical energotechnological system for processing apatite–nepheline ore wastes with the production of yellow phosphorus with consideration for the spatial and temporal interdependence between the chemical and energotechnological processes occurring in this system. One complex energy and resource efficiency criterion is the prime cost of electrical and heat energies, water, and coke spent on processing apatite–nepheline ore wastes in the considered chemical energotechnological system. It has been established that the optimal system functioning regime intensifies all the chemical energotechnological processes, decreases the energy and coke consumption, and increases the quality and purity of the finished product (i.e., yellow phosphorus). This study has resulted in a solution for the urgent scientific and practical problem of improving the energy and resource efficiency and environmental safety of a complex system for processing technogenic apatite–nepheline ore wastes via intensifying the chemical energotechnological processes occurring in this system.

Power and Resourse Efficient Envoronmentally Safe Technology for Processing Dumps of Technogenic Waste From Ore-Dressing and Processing Enterprises


 
 
 This research proposes a systematic approach for the analysis of volumes, physicochemical, granulometric, lithologic and thermal characteristics of waste from ore-dressing and processing enterprises stored in the dumps (tailing dumps) of ore-dressing and processing plants to assess the economic potential of its use in the system of complex power and resource efficient environmentally safe processing including palletizing machines, conveyor indurating machines and ore–thermal furnaces. The obtained results allow the authors to formulate the basic engineering, technological, economic and environmental requirements for complex chemical and power engineering systems of processing technogenic waste from ore-dressing and processing plants, these results make it also possible to define the degree of variability for the characteristics of the waste lots from various dumps. The paper describes the developed intensional and mathematical formulations for the multiscale problem of optimizing chemical and power engineering processes of technogenic raw materials processing in a complex chemical and power engineering system as a problem for discrete dynamic programming. The distinctive feature of this problem is to take into account the spatio-temporal multistage processing in a moving multilayer mass of pelletized raw material, the intensity of the process of internal moisture transfer and the variables for the control flow of the heat carrier gas. It allows increasing power efficiency by intensifying heat and mass transfer processes of multilayer drying, calcination and sintering. The criterion of the efficiency is the minimum cost of electric and thermal energy spent on processing. The obtained results were used to calculate power efficient environmentally safe processing of technogenic waste from ore-dressing and processing enterprises dumps. It was defined that heat and mass transfer processes are intensified, power consumption is reduced and the quality of the finished product is increased in the conditions of optimal power and resource efficient operation for the processing system.
 
 
 
 Keywords: tecnhogenic waste, waste processing, ore-dressing and processing plant, power and resource efficiency, optimization, system analysis, environmentally safety