Real Chemical Processes Research Articles

A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process

Slowly varying process variations represented by slow features, which reflect the inherent dynamics of chemical processes, are revealed to be advantageous for quality prediction. However, if slow features are extracted from process variables as predictors without the supervision of quality indices, some obvious disadvantages are observed: (1) low quality interpretation and redundant slow features since quality information is not considered for feature extraction; (2) a lack of analysis to investigate the relationship between slow features and quality interpretation, especially for different types of quality indices. To solve the above-mentioned problems, a new soft-sensor algorithm, quality-relevant slow feature regression (QSFR), is proposed in the present work. It defines a new objective function by concurrent consideration of slowness and quality interpretation, yielding more meaningful features as predictors to interpret quality index. On the basis of this, a critical feature selection strategy is proposed based on quality interpretation to determine the retained features for regression. Moreover, an in-depth analysis of the properties of retained features is provided to reveal the hidden mechanism and how the slow time-varying process variations influence the quality interpretation. This algorithm can extract more powerful features as predictors and enhance understanding of inherent nature of slow features. Finally, the feasibility and performance of the proposed method are well illustrated for a well-known benchmark process and a real chemical process. The developed QSFR algorithm performs better than traditional slow feature regression method, in which the values of RMSE of three specific quality indices have been reduced by 8.87%, 16.60%, and 3.40%, respectively.

A much better replacement of the Michaelis–Menten equation and its application

Michaelis–Menten equation is a basic equation of enzyme kinetics and gives acceptable approximations of real chemical reaction processes. Analyzing the derivation of this equation yields the fact that its good performance of approximating real reaction processes is due to Michaelis–Menten curve (8). This curve is derived from Quasi-Steady-State Assumption (QSSA), which has been proved always true and called Quasi-Steady-State Law by Banghe Li et al. [Quasi-steady state laws in enzyme kinetics, J. Phys. Chem. A 112(11) (2008) 2311–2321].Here, we found a polynomial equation with total degree of four [Formula: see text] (14), which gives more accurate approximation of the reaction process in two aspects: during the quasi-steady-state of the reaction, Michaelis–Menten curve approximates the reaction well, while our equation [Formula: see text] gives better approximation; near the end of the reaction, our equation approaches the end of the reaction with a tangent line the same to that of the reaction process trajectory simulated by mass action, while Michaelis–Menten curve does not. In addition, our equation [Formula: see text] differs to Michaelis–Menten curve less than the order of [Formula: see text] as [Formula: see text] approaches [Formula: see text].By considering the above merits of [Formula: see text], we suggest it as a replacement of Michaelis–Menten curve. Intuitively, this new equation is more complex and harder to understand. But, just because of its complexity, it provides more information about the rate constants than Michaelis–Menten curve does.Finally, we get a better replacement of the Michaelis–Menten equation by combing [Formula: see text] and the equation [Formula: see text].

Rayleigh-Bénard-Marangoni convection characteristics during mass transfer between liquid layers

Rayleigh-Bénard-Marangoni convection (RBM convection) induced by the mass transfer has a great influence on the performance of real chemical engineering process. However, the researches of RBM convection characteristics during mass transfer across the interface in liquid-liquid system and their influence on the interface morphology are still limited. In this research, a visualization experiment via the amplified shadowgraph method is conducted to investigate the mass transfer in water-toluene-acetone system in a vertical slit. The convective structure of RBM and its evolution are visually observed. The effects of the initial acetone concentration of aqueous phase and toluene phase, and the thickness of toluene layer on the RBM characteristics and the morphology of the liquid-liquid interface are investigated. The experimental results show that these structures are induced by the interface tension difference along the interface and the vertical density difference caused by non-uniform mass transfer at the interface. As a result of the mass transfer at the interface, the density stratification occurs at the top of the aqueous phase, where the light liquid layer supports heavy one. In addition, non-uniform mass transfer produces perturbation at the top of the aqueous phase, which induces the Rayleigh-Taylor instability at the " interface” between the heavy and light liquid layer. Consequently, a wave-shaped-mound " interface” in the upper aqueous phase is formed as the heavy liquid comes down into the light one, and it can be further evolved into a plume flow with the enhancement of the imbalance between density and pressure at the " interface”. Due to the difference in mass transfer characteristic caused by different concentration gradients in the plume " interface”, the plumes can also evolve into weak plumes and strong plumes. Under the large acetone concentration gradient, a number of RBM convective structures are generated near the interface in a short time and the convective cloud is formed due to the dramatic interaction and coalescence between these structures. With the weakening of mass transfer, the convective cloud disappears and the strong plume is gradually formed. In addition, the strength of RBM convection is demonstrated to be positively correlated with the acetone concentration gradient across the aqueous solution- toluene interface. In addition, the roughness of the interface and its unsteady fluctuation grow up with the increase of acetone concentration gradient across the interface.

Recent progress in the synthesis of inorganic particulate materials using microfluidics

Abstract Microfluidic systems have unique advantages originating from the miniaturization of real chemical processes. On this basis, the synthesis of inorganic materials, including micro- and nanomaterials, can be used to develop useful features, such as a uniform particle size distribution, morphology control, online monitoring, etc. In this review, we introduce and discuss the recent progress in the preparation of inorganic materials, mainly microfluidics systems. Furthermore, utilizing microfluidic devices, we present the basic principles and representative shape of microfluidic systems. In addition, the synthesis of various inorganic material particles using microfluidic systems, such as metal nanoparticles, metal oxide nanoparticles, semiconductor nanoparticles, organic-inorganic hybrid particles, surface functionalized particles, core-shell particles, and traditional porous materials, is introduced. Finally, we propose the future prospects of microfluidics in the synthesis of noble inorganic materials.

Design trade-offs in a column with side-reactor configuration for improving selectivity in multiple reaction systems

The configuration of columns with side-reactors (SRC) can effectively enhance the performance of reaction–separation processes, particularly where the operational limitations of conventional multifunctional reactors, such as reactive distillation systems, negate the overall benefits. Industrial manufacturing processes often use multiple reaction schemes that result in both desired and undesired products. The selectivity and yield of the desired product are usually improved by using large reaction vessels or high recycle flow rates of excess reactant. Therefore, in multiunit chemical processes, the foremost design trade-off is between the reaction vessel volume and the recycle flow rate. In this study, we investigate the competition of these parameters in SRC configurations in achieving specific conversion or yield criteria. Two real chemical processes involving side-reactors of non-reactive distillation columns and recycle streams are considered and their optimal design configurations are detailed. The results affirm the importance of this trade-off and its quantitative analysis to obtain the optimal SRC configuration.

Modeling Adsorption–Desorption Processes at the Intermolecular Interactions Level

Modeling of the surface adsorption and desorption processes, as well as the diffusion, are of considerable interest for the physical phenomenon under study in ground tests conditions. When imitating physical processes and phenomena, it is important to choose the correct parameters to describe the adsorption of gases and the formation of films on the structural materials surface. In the present research the adsorption-desorption processes on the gas-solid interface are modeled with allowance for diffusion. Approaches are proposed to describe the adsorbate distribution on the solid body surface at the intermolecular interactions level. The potentials of the intermolecular interaction of water–water, water–methane and methane–methane were used to adequately modeling the real physical and chemical processes. The energies calculated by the B3LYP/aug-cc-pVDZ method. Computational algorithms for determining the average molecule area in a dense monolayer, are considered here. Differences in modeling approaches are also given: that of the proposed in this work and the previously approved probabilistic cellular automaton (PCA) method. It has been shown that the main difference is due to certain limitations of the PCA method. The importance of accounting the intermolecular interactions via hydrogen bonding has been indicated. Further development of the adsorption-desorption processes modeling will allow to find the conditions for of surface processes regulation by means of quantity adsorbed molecules control. The proposed approach to representing the molecular system significantly shortens the calculation time in comparison with the use of atom-atom potentials. In the future, this will allow to modeling the multilayer adsorption at a reasonable computational cost.

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed.

Application of Economics Optimizing Control to a Two-step Transesterification Reaction in a Pilot-Scale Reactive Distillation Column

The challenges in the chemical process industry of tighter environmental and safety constraints, a higher economic efficiency and an operation in a more dynamic environment motivate the utilization of optimizing control where economic policies are integrated into a (often nonlinear) model predictive control scheme. This so-called one-layer approach or dynamic real-time optimization (D-RTO) has the advantage that the processes are dynamically steered towards the most profitable region. High-fidelity dynamic process models are a basic prerequisite for a good controller performance, and building such models is a challenge. Using highly complex models also may lead to long computation times and thus feedback delays. These issues are in practice avoided by applying only steady-state optimization based on nonlinear models (RTO) and/or using simplified models in MPC. However, the development of computational methods that are able to solve large-scale dynamic optimization problems efficiently have paved the way for applications of economics optimizing control to complex chemical processes. In this contribution, we will demonstrate that a real complex pilot-scale chemical processes, a two-step transesterification realized by reactive distillation that is described by a large DAE model can be operated at the economic optimum by using direct optimizing control. We discuss the problem formulation and the numerical methods used and show experimental data that were obtained at the real process.

A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment

Industrial chemical plant diagnosis is the task of analyzing process data to sufficiently pinpoint the causes of abnormal events as fast and as accurately as possible so corrective action can be taken in a timely manner. The need to identify failures explicitly and support human centered decision making becomes pronounced for enterprises. Qualitative diagnostic models offer robustness in capturing diagnostic behaviors when there is little or no data on fault conditions. This article develops, analyzes, and demonstrates a qualitative diagnostic methodology called Causal Link Assessment (CLA). CLA avoids the drawbacks of other methodologies while leveraging several new concepts that include dynamic pattern generation, single time step modeling with multitime step interpretation, and discretized, low granularity dynamic modeling. CLA is demonstrated for an existing ethylene production facility. Model building, robustness, reusability, unaccounted for faults and failures and alignment with emerging Smart Manufacturing infrastructure concepts are discussed. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3420–3431, 2016

Abnormal situation management: Challenges and opportunities in the big data era

Abstract Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM.

Chemical looping dechlorination through adsorbent-decorated Fe2O3/Al2O3 oxygen carriers

Plastic waste incineration is the main source of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), which, as toxic materials, have aroused increasing attention all over the world. Based on the formation mechanisms of PCDD/Fs, the key approaches to eliminate PCDD/Fs are to maintain an oxygen-deficient atmosphere to suppress the de novo route of PCDD/Fs formation and to constrain the presence of free chlorine to prevent the formation of PCDD/Fs from the conversion of precursors (e.g., unchlorinated benzene and phenol). Chemical looping combustion (CLC), which utilizes active lattice oxygen provided by an oxygen carrier (OC) for fuel oxidation in the absence of O2 was, for the first time, proposed as a promising pattern for disposing plastic waste. To constrain the presence of free chlorine, the feasibility of dechlorination through adsorbent-decorated Fe-based OC during the CLC process was investigated through thermodynamic simulation, isothermal kinetic experiments in a thermogravimentric analyzer (TGA), and batch fluidized bed reactor experiments. For three CaO/K2O/Na2O adsorbents, CaO decoration for OC particles exhibits the highest dechlorination efficiency, however it results in the maximum activation energy in the TGA experiments and relatively more carbon deposition on the OC particles in these batch fluidized bed experiments. Subsequently, the effects of the CaO decoration method (wet impregnation, coprecipitation, and physical mixture), CaO loading and reaction temperature on the efficiencies of dechloridation and combustion were investigated in the batch fluidized bed reactor that simulates a real chemical looping process. The results indicated that the wet impregnation method demonstrates the best adsorbent decoration and that 5 wt% CaO loading reaches an optimized level of dechloridation. In addition, increasing the temperature can improve the dechlorination, but it also increases carbon deposition, leading to a lower combustion efficiency. Next, the dechloridation efficiency gradually descends, and correspondingly, the mole ratio between Ca and Fe on the OC particle surface exhibits a declining trend with an increase in the cycle number; these results were verified by ion chromatography and an environmental scanning electron microscope coupled with energy dispersive X-ray spectroscopy (ESEM-EDX), respectively. Notably, the ESEM-EDX result demonstrates the accumulation of Cl elements onto the surface of OC particles. Last, the reduced samples were washed using a dilute hydrochloric acid solution after 30 cycles, and the ESEM-EDX result demonstrates the removal of the Ca and Cl elements from the surface of these OC particles and the regeneration of these OC particles. Overall, these results provide sound evidence for the feasibility of chemical looping dechlorination through the use of adsorbent-decorated Fe2O3/Al2O3 OC particles.

Optimization of process conditions in a tubular rotary kiln with applying TG/DSC technique and mathematical modeling

The report examines the possibilities of applying TG/DSC, DSC and heat flux (Calvet) calorimetry techniques to the kinetic study of complicated multistage industrial reactions. The informativeness of calorimetric data and the procedure of their joint treatment together with analytical data for solving inverse kinetic tasks have been envisaged. The results and experience of applying DSC and calorimetric data for developing mathematical models of real industrial chemical processes and further process optimization are presented and discussed.

Electrical/Dielectric Measurements for Monitoring Polymerization, Morphology and Mechanical Integrity in Polymer Nanocomposites

Electrical and dielectric measurements are a powerful tool for following in real time physical and chemical processes, evolution of morphology and mechanical integrity in complex polymeric systems. Here we employ electrical dc measurements and broadband dielectric spectroscopy (DS) to follow the state of polymerization and curing processes in polymer/clay nanocomposites and the quality of filler dispersion, alignment and integrity of carbon nanotubes (CNTs) in various polymer/CNT nanocomposites, focusing on fundamental aspects of the various processes involved.

CALCULATION OF THE BINARY INTERACTION AND NONRANDOMNESS PARAMETERS OF NRTL, NRTL1, AND NRTL2 MODELS USING GENETIC ALGORITHM FOR TERNARY IONIC LIQUID SYSTEMS

One of the most important applications of thermodynamics is the accurate prediction of fluid phase equilibria problems related to real chemical engineering processes. Various equations of state as well as activity coefficient models have been developed for such calculations with many interaction, size, and randomness parameters, which should be optimized based on powerful and effective computational methods. Leading to globally optimal values, genetic algorithm (GA) as a powerful and effective tool can be used for prediction of the interaction parameters of thermodynamic models in complex liquid-liquid equilibrium (LLE) systems. It requires only lower and upper bounds for the interaction parameters and the necessary initial guesses are produced automatically. In the present work, based on the GA method, a global optimization procedure is introduced for calculation of the binary interaction and nonrandomness parameters of NRTL, NRTL1, and NRTL2 activity coefficient models for 20 ternary aromatic extraction systems containing 16 different ionic liquids at various temperatures. The values of the parameters along with the root-mean-square deviations (rmsd) are reported. The results, in terms of rmsd for NRTL, NRTL1, and NRTL2 models, are very satisfactory, with global values of 0.0031, 0.0020, and 0.0053 for 187 tie-lines respectively. The obtained rmsd values for the NRTL model using the GA method are better than those reported in the literature. The rmsd results for the three studied models show that NRTL1 can handle the LLE calculations with more accuracy than the original NRTL and NRTL2 activity coefficient models.

Effect of ZnO nanoparticles on nasopharyngeal cancer cells viability and respiration

Development of a therapeutic drugs based on nanoparticles requires a better understanding of the mechanism of selective cyto-toxic effects of nanopaticles over cancer cells. Scanning electrochemical microscopy provides opportunity to measure the real time chemical process at cell proximity in the presence of nanoparticle. Herein, the respiration process in nasopharyngeal cancer cells is investigated with the help of scanning electrochemical microscopy. The cell viability has been tested with MTT assay. The results show that ZnO nanoparticles have time and dose dependent effect in nasopharyngeal cancer cells and the cell respiration rate decreases with time.

Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like Kp, Ki, Kd, and α with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems

Common vulnerabilities of RTO implementations in real chemical processes

AbstractReal‐time optimisation (RTO) systems face challenging scenarios in industrial practice, such as incomplete and corrupted process information, uncertain large‐scale mathematical models and numerical optimisation issues. Proper design of RTO structure and robust diagnosis tools are keys for good performance, although they are neglected in commercial RTO software and not fully solved in the technical literature. This article reviews the concepts behind the two‐step RTO approach and suggests performance metrics. It also points out the large list of structural decisions and the consequences of intuitive, experience‐based RTO design choices. The discussed vulnerabilities are illustrated with some simulations and real industrial implementations. © 2012 Canadian Society for Chemical Engineering

Industrial wastewater collection using a separation technique

Water is an essential element within normal functioning throughout the chemical industry. The reuse of water, or more precisely water condensate collection, is very important during real chemical processes because it can reduce raw materials, energy losses, and costs, and can improve the operations of energy and process systems.The effect of industrial activity on the environment has attracted considerable attention over the past few decades. Industry, therefore, has started looking at methods of reducing the volumes of produced wastewater. This paper presents the industrial separation of lower and higher temperature condensates’ collection using the heating utility of steam production, by separation techniques. The existing condensate collection regarding utilities’ preparation for steam-generation may no longer be optimal; the basic intention is that minimal changes in the system can produce an efficient improvement by using separate collections of low and high-temperature condensates, and the use of available heat with little heat flow rate loss. This separated water condensate collection preparation for the utility of steam production by using a separation technique and MINLP (mixed-integer nonlinear programming) was tested on an existing methanol process, which allowed for an efficient and additional 7% water condensate collection system for steam-generation.

10.2478/s11814-009-0253-0

The main purpose of this study was to develop a computer automated tool for fault tree analysis (FTA) in order to minimize the flaws of manual FTA. The automated FTA system developed in this study consists of two steps: 1) automatic fault tree conversion from a digraph, and 2) calculation of the probability of the occurrence of the top event and finding a minimal cut set of the top event. For the first step, we propose a new algorithm for automatic conversion of a digraph to a fault tree. The new digraph-FT conversion algorithm has eight FT generation rules to transform node information that is based on the node characteristics. Failures and faults are classified into three types to easily synthesize fault trees and analyze fault trees precisely. The automatic FTA system was then applied the analysis of real chemical processes to illustrate the effectiveness of the system.

Synthesis of hydrocarbons under upper mantle conditions: Evidence for the theory of abiotic deep petroleum origin

A theory of abiotic deep petroleum origin explains that hydrocarbon compounds are generated in the upper mantle and migrate through the deep faults into the Earth's crust. There they form oil and gas deposits in any kind of rock in any kind of the structural position. Until recently one of the main obstacles for further development of this theory has been the lack of reliable and reproducible experimental results confirming the possibility of the spontaneous synthesis of complex hydrocarbon systems at high pressure and temperature. Our experimental results demonstrate that abiotic synthesis of hydrocarbons under mantle conditions is a real chemical process. Different paths of hydrocarbon synthesis under mantle conditions are discussed. Obtained experimental results place the theory of the abiotic deep petroleum origin in the mainstream of modern experimental physics and physical chemistry.