Material Balance Equations Research Articles

ENERGY EFFICIENCY PROCESS OF ANAEROBIC FERMENTATION OF ORGANICS WASTE

Anaerobic fermentation in biogas plants is an effective and promising method of manure disposal. During fermentation, especially in the thermophilic mode, animal excrement is largely disinfected, pathogenic microflora dies, weed seeds lose their germination and, in addition, biogas is released, the utilization of which allows to obtain additional energy. This way of utilization of manure under the conditions of gradual depletion and increase in the price of traditional sources acquires special importance. Since fermentation is carried out in closed facilities, completely isolated from the surrounding environment, the possibility of the spread of infections and gases with an unpleasant smell and the ingress of untreated manure into the soil, ground and underground water is eliminated. As a result of the decomposition of organic substances in the fermented product, the share of biogenic elements increases, most of them turn into mineral substances easily absorbed by plants, and nitrogen losses do not exceed 3-5%. An experimental installation was developed to determine the nature of gas release, the depth of mineralization of organic matter depending on the regime and design parameters, as well as with the purpose of developing, based on the obtained dependencies, technological schemes for anaerobic treatment and waste disposal of animal husbandry enterprises. The theoretical basis of mathematical modeling and forecasting of the operation of bioenergy plants is the material balance equation related to the rates of biochemical reactions and flow rates. The derivation of the material balance equation is possible if there is information about the hydraulic characteristics of bioenergy reactors, which depend on the operating conditions, limitations, and final results of the process. In addition, data on the rate of flow of biochemical reactions, the influence of environmental conditions and operating parameters on them are also necessary.

МОДЕЛИРОВАНИЕ ПРОЦЕССОВ ВЛАГОПОГЛОЩЕНИЯ ДРЕВЕСИНЫ ПРИ ИЗМЕНЕНИИ ДАВЛЕНИИ СРЕДЫ

On the basis of differential equations of material balance in a hermetic chamber and heat and mass transfer in the object under study under given boundary conditions, a mathematical model describing technological processes occurring with changes in medium pressure has been developed. The methodology of experimental studies of the process of material saturation in a unit operating in a periodic mode at varying medium pressure is presented. Birch veneer is taken as a model material for experimental studies, the heat and mass transfer characteristics of which are widely known. The preparation of the experiment consisted in the production of samples of the saturated material, in the form of rectangular plates with dimensions of 30 * 90 mm and a thickness of 4 mm. The results of physical and mathematical modeling are reflected in the form of profiles of changes in moisture content and temperature along the cross section of the material during saturation. It is established that the active process of saturation of the veneer with moisture under changing medium pressure is observed during the first 15 minutes, and by this time the temperature in the center of the material exceeds 120 ° C. The results of mathematical modeling were obtained using the Visual Basic for Applications software environment. The discrepancy between the calculated and experimental data does not exceed 15%. The developed mathematical model of the process makes it possible, when the capacities of sources (steam generator) and drains (condenser and vacuum pump) vary, to recommend the modes of conducting the technological process or to select the capacities of the steam generator and pump in accordance with the required law of pressure change in the technological process. For physical modeling of the investigated process in the processing of wood raw materials, an experimental setup was created, on which experimental data confirming the adequacy of the developed model were obtained.

Einstein material balance and modeling of the flow of compressible fluid near the boundary

We consider sewing machinery between nite difference and analytical solutions de ned at different scales: far away and near the source of the perturbation of the flow. One of the essences of the approach is that the coarse problem and the boundary-value problem in the proxy of the source model two different flows. In his remarkable paper, Peaceman proposes a framework for dealing with solutions de ned on different scales for linear time independent problems by introducing the famous Peaceman well block radius. In this article, we consider a novel problem: how to solve this issue for transient flow generated by the compressibility of the fluid. We are proposing a method to glue solution via total fluxes, which are prede ned on coarse grid, and changes in pressure, due to compressibility, in the block containing production (injection) well. It is important to mention that the coarse solution “does not see” the boundary. From an industrial point of view, our report provides a mathematical tool for the analytical interpretation of simulated data for compressible fluid flow around a well in a porous medium. It can be considered a mathematical “shirt” on the Peaceman well-block radius formula for linear (Darcy) transient flow but can be applied in much more general scenarios. In the article, we use the Einstein approach to derive the material balance equation, a key instrument to de ne R0. We will expand the Einstein approach for three regimes of Darcy and non-Darcy flows for compressible fluids (time-dependent): 1. stationary; 2. pseudostationary; 3. boundary dominated. Note that in all known authors literature, the rate of production on the well is time-independent.

Direct calculation of the equilibrium composition for multi-component Langmuir isotherms in batch adsorption

The extended Langmuir isotherm is commonly used to describe multi-component adsorption. In batch adsorption, the equilibrium composition can be computed by solving simultaneously the Langmuir equations and the material balance equations numerically. We demonstrate that for given values of the initial fluid-phase concentrations, the adsorbed phase composition of all components can be expressed in terms of the fluid-phase concentration of a reference species. As a result, the adsorbed phase composition is easily derived by combining the isotherm expression for the reference species with the corresponding material balance. We derive a general graphical representation of the underlying mathematics valid for N components along with the analytic solution for two-component systems.

Design of Double Tank Liquid Level Control System Based on Fuzzy PID

Liquid level is one of the main parameters in industrial production process control. There are many problems such as interference and delay in the dual capacity water tank. It is usually difficult to meet the strict production requirements with the conventional PID control scheme. The corresponding transfer function is firstly deduced through the material balance equation in this article. By testing, the performance of conventional PID needs to be further improved. Then build the fuzzy PID model in Matlab/simulink and carry out simulation verification. By comparing with the PID simulation effect, the results show that under fuzzy PID control, the overshoot of the system is effectively reduced and the adaptability is strong. It reflects that the control strategy has a good reference value.

From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

A Semi-Analytical Model for Production Prediction of Deep CBM Wells Considering Gas-Water Two-Phase Flow

The productivity prediction of deep coalbed methane (CBM) wells is significantly influenced by gas-water two-phase flow characteristics and seepage parameters of the fracture network. While numerical simulations offer a comprehensive approach, analytical models are favored for their faster and broader applicability. However, conventional analytical models often oversimplify the complex problem of two-phase seepage equations, leading to substantial errors in dynamic analysis outcomes. Addressing this shortcoming, we establish a gas-water two-phase productivity prediction model for deep CBM reservoirs. This model takes into account the two-phase flow characteristics within the reservoir and fracture network, as well as the stress sensitivity of the reservoir and fractures. Additionally, a modified trilinear flow model characterizes the fractured modification body. By integrating the flowing material balance equation with the Newton Iteration method, we gradually update the seepage model’s nonlinear parameters using the average formation pressure. We also linearize the gas-water two-phase model through successive iterations to derive a semi-analytical solution. The accuracy of the model was verified through comparison with commercial numerical simulation software results and field application. The model also enabled us to scrutinize the influence of reservoir and fracture network parameters on productivity. Our research findings suggest that the semi-analytical solution approach can efficiently address the nonlinear seepage problem of gas-water two-phase flow, enabling quick and accurate prediction of deep CBM well productivity. Moreover, appropriate fracture network parameters are paramount for enhancing the productivity of deep CBM wells. Lastly, during the development of deep CBM reservoirs, it is crucial to control the production pressure difference appropriately to minimize the stress sensitivity impact on production capacity.

CRM-Aquifer-Fractional Flow Model to Characterize Oil Reservoirs with Natural Water Influx

Summary This work presents a new flow model that uses production data to characterize conventional undersaturated oil reservoirs with natural water influx in primary production. The main application is to estimate the original oil in place (OOIP), but the model also calculates the productivity index (PI), water influx, and heterogeneity factor. Aquifers are common to many reservoirs and their diagnosis is relevant because they can serve as an important drive mechanism but can also affect well productivity. Discerning between the reservoir and aquifer volumes is also a major challenge in many oil fields. Reservoir-aquifer systems have been investigated with various dynamic characterization techniques such as the material balance equation (MBE), pressure transient analysis, and production/rate transient analysis. These methods have limitations, for example, requiring shut-in wells and/or very large production histories and high mobility and compressibility ratios to yield distinguishable responses in the diagnostic plots. The new flow model couples the producer-based capacitance resistance model (CRMP) with the Fetkovich aquifer model (CRMPA). It enables calculating the total instantaneous production flow rate from a well as a function of three mechanisms—reservoir depletion, changes in bottomhole pressure (BHP), and water influx. In addition, CRMPA is coupled with the Koval fractional flow model (capacitance resistance aquifer-fractional flow model, CRMPAF) to calculate the individual oil and water rates and to enhance the OOIP estimation for wells with two-phase production caused by water breakthrough from the aquifer. The capacitance resistance model (CRM) is a production analysis technique that uses rate and pressure data from typical field surveillance (no dedicated tests) to characterize reservoir properties. It has been widely used to study reservoirs under secondary and tertiary recovery. There are a few applications for primary recovery, and only one reference investigated natural water influx. There are important differences between the previously published and the new CRMPA formulations. The former estimated the individual water influx to each well in a multiwell reservoir associated with an aquifer. The calculations were performed assuming the reservoir pore volume (PV) was known, whereas the present approach simultaneously calculates the PV and the water influx. The former approach also required a priori knowledge of the average pressure, obtained from buildup tests, whereas the new method uses production data, (i.e., it does not require shut-in wells). In the new approach, CRMP (without aquifer) is first used to calculate the capacitance/resistance (CR) parameters of the equivalent (reservoir-aquifer) medium. It provides the correct OOIP for volumetric reservoirs; however, CRMPA is used when the PV significantly differs from volumetrics and water influx is detected. CRMPA calculates the reservoir CR parameters using simple relationships between the equivalent and composite mediums. For wells with two-phase production, the Koval factor is an additional parameter. The CRMPA/F solution is obtained by minimizing the errors between the calculated and measured rates over a time window. The new CRMPA/F approach is compared and validated with several models of single and multiwell synthetic reservoir-aquifer systems with different properties and geometries. It is also used to characterize a field case.

Study on development methods of different types of gas wells in tight sandstone gas reservoirs

Reasonable production allocation of tight sandstone gas reservoirs is an important basis for efficient development of gas wells. Taking Block XX in Ordos Basin as an example, the modified flowing material balance equation was established considering the variation of gas viscosity and compression coefficient, the advantages and disadvantages of the method were discussed, and a reasonable production allocation process for gas wells was developed. The results show that: ① The commonly used flow material balance method ignores the change of natural gas compression coefficient, viscosity and deviation coefficient in the production process. The slope of the relationship curve between bottom hole pressure and cumulative production and the slope of the relationship curve between average formation pressure and cumulative production are not equal After considering this change. Compared with the results calculated by the material balance method, the results calculated by the flow material balance method are smaller. ② The production of 660 gas wells in the study area during stable production period is verified. Compared with the open flow method, the dynamic reserve allocation method is better, with an error of 0.06%. ③ The new method in this paper is used to allocate production for different types of gas wells. The cumulative production of different types of gas wells shows different degrees of increase. The I, II, III and IV types of gas wells increase by 32.26%, 30.29%, 23.58% and 25.07% respectively. This study provides technical support for dynamic reserve calculation and reasonable production allocation of gas wells in the study area, and has important guiding significance for the formulation of reasonable development plan and economic and efficient development of tight sandstone gas reservoirs.

Hybrid deep modeling of a CHO-K1 fed-batch process: combining first-principles with deep neural networks.

Introduction: Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on three-layered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power. Methods: This study compares, for the first time, deep and shallow hybrid modeling in a CHO process development context. Data of 24 fed-batch cultivations of a CHO-K1 cell line expressing a target glycoprotein, comprising 30 measured state variables over time, were used to compare both methodologies. Hybrid models with varying FFNN depths (3-5 layers) were systematically compared using two training methodologies. The classical training is based on the Levenberg-Marquardt algorithm, indirect sensitivity equations and cross-validation. The deep learning is based on the Adaptive Moment Estimation Method (ADAM), stochastic regularization and semidirect sensitivity equations. Results and conclusion: The results point to a systematic generalization improvement of deep hybrid models over shallow hybrid models. Overall, the training and testing errors decreased by 14.0% and 23.6% respectively when applying the deep methodology. The Central Processing Unit (CPU) time for training the deep hybrid model increased by 31.6% mainly due to the higher FFNN complexity. The final deep hybrid model is shown to predict the dynamics of the 30 state variables within the error bounds in every test experiment. Notably, the deep hybrid model could predict the metabolic shifts in key metabolites (e.g., lactate, ammonium, glutamine and glutamate) in the test experiments. We expect deep hybrid modeling to accelerate the deployment of high-fidelity digital twins in the biopharma sector in the near future.

Modeling and experimental study of hollow cylindrical catalysts for prediction of transient behaviors of suspension photoreaction systems by Langmuir-Hinshelwood kinetics

Effectiveness factor was derived for hollow cylindrical pellets with finite length by assuming Langmuir-Hinshelwood (LH) kinetics. Transient intra-particle concentration was predicted by solving reaction-diffusion equations using finite element method for various values of reaction parameters in LH kinetics and thickness of hollow core. At stationary state, numerical values of intra-particle concentration was integrated to calculate effectiveness factor (η) under various shape factors of hollow cylindrical pellet including infinite length as well as finite height. The results were compared with those of infinitely long cylinder and spherical pellets to determine optimum morphologies of catalytic pellets. η could be enhanced by increasing hollow-core size and decreasing aspect ratio of the pellet. For the same value of Thiele modulus, which was calculated from volume-equivalent radius, hollow cylindrical catalyst exhibited largest value of η, compared to solid cylindrical or spherical pellets. For simulation of catalytic reactors such as batch reactor, CSTR, and fixed bed, pseudo-steady state was adopted in material balance equation using computed values of η. The modeling results were compared with experimental data from batch photocatalytic reactor containing hollow catalytic fibers synthesized by electrospinning to determine reaction parameters in LH kinetics by minimizing errors of transient concentration between calculated and measured values using optimization method.

Estimation of Free and Adsorbed Gas Volumes in Shale Gas Reservoirs under a Poro-Elastic Environment

Unlike conventional gas reservoirs, fluid flow in shale gas reservoirs is characterized by complex interactions between various factors, such as stress sensitivity, matrix shrinkage, and critical desorption pressure. These factors play a crucial role in determining the behavior and productivity of shale gas reservoirs. Stress sensitivity refers to the stress changes caused by formation pressure decline during production, where the shale gas formation becomes more compressed and its porosity decreases. Matrix shrinkage, on the other hand, refers to the deformation of the shale matrix due to the gas desorption process once the reservoir pressure reaches the critical desorption pressure where absorbed gas molecules start to leave the matrix surface, causing an increase in shale matrix porosity. Therefore, the accurate estimation of gas reserves requires careful consideration of such unique and complex interactions of shale gas flow behavior when using a material balance equation (MBE). However, the existing MBEs either neglect some of these important parameters in shale gas reserve analysis or employ an iterative approach to incorporate them. Accordingly, this study introduces a straightforward modification to the material balance equation. This modification will enable more accurate estimation of shale gas reserves by considering stress sensitivity and variations in porosity during shale gas production and will also account for the effect of critical desorption pressure, water production, and water influx. By establishing a linear relationship between reservoir expansion and production terms, we eliminate the need for complex and iterative calculations. As a result, this approach offers a simpler yet effective means of estimating shale gas reserves without compromising accuracy. The proposed MBE was validated using an in-house finite element poro-elastic model which accounts for stress re-distribution and deformation effects during shale gas production. Moreover, the proposed MBE was tested using real-field data of a shale gas reservoir obtained from the literature. The results of this study demonstrate the reliability and usefulness of the modified MBE as a tool for accurately assessing free and adsorbed shale gas volumes.

ANALYTICAL EXPRESS CALCULATION OF THE MAIN INDICATORS OF THE DEVELOPMENT OF A NEW OIL AND GAS RESERVOIR FOR MULTIVARIATE CALCULATIONS IN ORDER TO OPTIMIZE DESIGN SOLUTIONS

The assets of Rosneft Oil Company PJSC include oil and gas reservoir that have not been put into development, in addition, the resource base is being replenished through geological exploration. The use of new technologies in exploration and production, the introduction of new software modules for calculations, tax regimes for oil production create prospects for increasing production from oil and gas reservoir that have not been put into development in the whole of the Russian Federation. At the stage of decision-making on putting new assets into development, it is required to be able to conduct an express assessment of technological indicators.
 This paper presents the results of the formation of analytical approaches for carrying out express calculations of the development indicators of oil and gas, gas-oil and oil-gas condensate reservoir that have not been put into development.
 The calculations are based on the use of a set of pre-calculated displacement characteristics and the material balance equation of hydrocarbon reservoir. All developed algorithms are implemented in a new software module, which is an integral part of the RN-KIN corporate information system, the main tool for a development engineer at Rosneft Oil Company PJSC.
 This paper reflects the mathematical description of the algorithm for the express calculation of development indicators, the main tasks that were solved in the course of the work, and the results obtained.

Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition.

A semi-analytical model for quantifying the inter-well communication in water-bearing shale gas-condensate reservoirs

The increased risk of well communication in unconventional reservoirs expands the requirement for effective techniques to quantify the strength of inter-well communication. This work develops a robust and practical semi-analytical model to interpret the parent-child well system communicating through the fracture hits in water-bearing shale gas-condensate reservoirs. The compartment concept is employed to simplify the parent-child well system, in which the fracture hit region (FHR) is applied to describe the communication between sibling wells. The improved distance of investigation (DOI) is derived to characterize dynamic drainage under the three-phase condition for the water-bearing shale gas-condensate reservoirs. The material balance equations (MBEs) for each phase (oil, gas, and water) in each region are coupled and solved simultaneously to obtain three-phase production. The average pressure and saturation in each region are updated during the iteration with adaptive timestep to reduce the effect of nonlinearity under three-phase condition. Furthermore, the genetic algorithm (GA) is introduced to realize the automatic history match and properties interpretation of the parent-child well system.The oil/gas production of parent well drops rapidly when the child well starts to produce indicates the fracture-drive interference. However, similar drop in water production rate sometimes doesn't take place due to higher water saturation caused by fracturing of the child well. By the production history matching of field data from southwest Canada, the water and oil saturation, half-length and permeability of fractures, matrix permeability and FHR parameters are interpreted, higher efficiency than the numerical simulations. The results demonstrate the practical application of the new model for reservoir and fracture properties from online production data of parent-child well system.

Estimation of Shale Gas Reserves: A Modified Material Balance Equation and Numerical Simulation Study

This study presents a comprehensive material balance equation (MBE) to estimate the reserve of shale gas reservoirs including free and adsorbed gas volume. The presented material balance equation takes into account the effect of stress change, matrix shrinkage, water volume production and influx, and critical desorption pressure. The material balance equation is converted into a linear relationship between the reservoir production and expansion parameters used during the derivation procedures that include rock-fluid properties and production history data. The proposed straight line reserve evaluation technique yields a slope of original free and absorbed gas in organic matrix, while the y-intercept yields the volume of original free gas in the in-organic matrix. A field case study of shale gas located in Australia is presented. Results show that the proposed MBE and the corresponding straight line reserve evaluation technique are rational and competent in estimating the free gas and adsorbed gas volumes accurately with error less than 6% compared to the numerical simulation model presented in this study using an in-house simulator based on finite element technique and FORTRAN language. Hence, the presented technique in this study can be used as a quick and easy to use tool to accurately estimate the free and adsorbed gas reserves and to improve the development of the production strategies of shale gas reservoirs.

Modeling of Core-shell Catalytic Pellets of Inert Shell with Various Morphologies for Series Reactions

Modeling of core-shell catalytic pellets with inert shell was performed assuming three different particle shapes such as sphere, cylinder, and slab. For isothermal irreversible first order reaction, A→B, analytical solutions of intra-particle concentration of reactant were derived by solving reaction-diffusion equations, considering thickness of catalytically active core (r2), when the pellets were immersed in infinitely large medium. Unlike conventional pellets or core-shell pellets with inert core, effectiveness factor (η) was affected by r2 as well as ratio of effective diffusivity in inert shell and active core (Υ), and η could be enhanced by increasing Υ. η increased in the order of sphere > cylinder > slab due to surface area per unit volume of medium, and η increased with increasing Biot number (Bi), because of decreased external film resistance. For series reaction, A→B→C, transient change of the concentration of starting reactant (CA) and intermittent product (CB) in bulk fluid of reactor could be predicted by solving material balance equations assuming pseudo-steady state approximation and equal value of Thiele modulus for both reactions in batch reactor and CSTR. When B is desirable product, CB increased by increasing Υ and decreasing r2. Similar to conventional pellets, CB in batch reactor decreased by increase of distribution coefficient of B on surface of pellet, KB, due to increased adsorption, and increased in the order of sphere < cylinder < slab, similar to conventional pellets. In CSTR, CB at steady state could be enhanced by decreasing amount of catalyst and Thiele modulus (Φ), implying that performance of reactor can be controlled by adjusting various reaction parameters. From numerical solutions of reaction diffusion equations for series reactions, the effect of r2 and Υ were also confirmed from transient value of CA and CB in exit stream calculated by finite element method.

Development of a mathematical model for a compound technological complex of vanyukov melting in order to control the material and thermal regime

This article presents a mathematical model in the form of static equations of dependencies of input and output flows based on the equations of material and heat balance for the purposes of operational planning and control of the complex technological complex of Vanyukov melting (PV). Dynamic characteristics are presented for the purpose of controlling the thermal regime based on the technology of the developed melting process with blowing from below. As a result of the study, the developed mathematical model for controlling the smelting process when calculating the material flows of the charge will allow tracking changes in the thermal state of the smelting (by the copper content in the matte). This model can quite well describe the dynamics of the state of the process, both when establishing the impacts aimed at increasing the heating of the furnace, and at reducing its heating. Based on the equations, a computer model based on the dynamic programming method in the MATLAB software package has been developed. The scientific novelty lies in the fact that for the first time, the structure of a mathematical model has been developed that describes the processes occurring in the over-tuyere zone and the sludge zone of the smelting products.

Повышение эффективности пиролизной переработки твердых коммунальных отходов

The disposal of waste by direct combustion method is prohibited in the Russian Federation due to the negative impact on the environment. An alternative option is solid waste pyrolysis technology, which allows not only to significantly reduce the amount of waste that requires disposal, but also to obtain various types of energy carriers. Existing installations allow recycling waste with humidity up to 30–35 %. An increase of humidity over 40 % requires additional energy sources for the drying process. To increase the efficiency of waste recycling by pyrolysis, it is necessary to improve the technology and to design an energy-efficient waste disposal plant. The use of the heat of combustion products in the heat exchanger to heat the air going into the drying chamber will increase the range of processing of wet waste. To determine the efficiency of the furnace for thermal waste disposal, the method of material and thermal balance is used. It allows determining the efficiency of the installation and selecting its operating mode with its maximum value. The paper proposes a new design of a waste disposal furnace with separate drying and pyrolysis chambers, as well as a mathematical model based on the equations of thermal and material balance. The design feature of the installation allows you to organize the controlled drying of wet waste and reduce losses with combustion products by using a heat exchanger for air heating. The proposed installation allows the waste recycling process to be carried out at a relative humidity of 0 to 60 % without additional energy sources. The efficiency of the waste varies from 61,5 to 80 % when the plant is operating on dry waste and from 42 to 62 % when operating on wet waste.

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.