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In this research new derivatives of pyrroles in good yields were prepared using multicomponent reactions of amoxicillin, activated acetylenic compounds, and alkyl bromides in water as solvent at room temperature in the presence of catalytic amounts of active carbon. The short time of reaction, high yields of product, and easy separation of them are some advantages of this procedure. In this research, the adsorption process was investigated for cefixime and amoxicillin as antibiotic drugs removal using active carbon and zeolite as organic and natural adsorbents in continuous and batch reactors in lab-scale, and the experiments were continued continuously using an adsorbent that absorbed more the two drugs. According to obtained results, the active carbon adsorbed both cefixime and amoxicillin but the zeolite did not adsorb the amoxicillin but adsorbed the high percentage of cefixime in the batch procedure. It can be concluded that some advantages of the continuous process compared to batch reactor are high performance of adsorbent, short time of process, and high percentage of adsorption. Green chemistry is the use of a set of principles to reduce or eliminate the use or generation of unsafe materials in the design, fabrication, and applications of chemical products. Among solvents, water is a green solvent and very suitable for performing organic reaction. The present procedure avoids the use of toxic solvents. According to obtained results, the active carbon adsorbed both cefixime and amoxicillin, but the zeolite did not adsorb the amoxicillin but adsorbed the high percentage of cefixime in the batch procedure. [ABSTRACT FROM AUTHOR]

Product quality is paramount for companies to maintain the trust from their customers. Powder products and in particular fertilizers can suffer damage during storage if the temperature and moisture are not appropriate. Most of the work has focused on drying, but the last stage of cooling is responsible for avoiding product out of specification. A first principles model has been developed for industrial fluidized vibrating cooling units following a multiscale approach, from the particle to the entire unit, to evaluate their operation. The unit consists of two sections using atmospheric and cool air, respectively. A refrigeration cycle is also modelled to compute the needs to cool the air. The unit's model only includes two adjustable parameters, the heat transfer efficiency from the particle to the air as well as for the losses from the unit to atmosphere. The model has been validated using industrial data, resulting in heat flow efficiency from the particle equal to 0.30 and the flow of losses of 1.17 kW/K. The model can reproduce the industrial data within reasonable error and allows predicting the cooling cycle needs and estimating the cost of its operation as a function of the weather. The location of the facility results in the fact that during June-August the atmospheric air cannot cool the product below the critical temperature and the refrigeration cycle needs to operate. • Product features determine the customer acceptance. • An industrial vibrocooling unit has been modelled and validated. • The model is based on first principles with few adjustable parameters. • The model allows predicting the unit operation over a year time. • June-August requires the use of the unit for the product to reach specifications. [ABSTRACT FROM AUTHOR]

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application "ERT-Vis" is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution–solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data. [ABSTRACT FROM AUTHOR]

• Microscopic modeling of atomic layer etching. • Calculation of reaction rate parameters via first-principles. • Computationally-efficient machine learning model development. • Real-time calculation of optimal operating conditions. With increasing demands for microchips and increasing needs in the nano-scale semiconductor manufacturing industry, atomic layer etching (ALE) has been developing into a critical etching process. Unlike its counterpart in the film deposition domain, atomic layer deposition (ALD), which has been extensively studied, ALE has not been fully studied yet from a modeling and operation point of view. Therefore, this work develops microscopic models to characterize the thermal ALE process of aluminum oxide thin films with two precursors (hydrogen fluoride and trimethylaluminum). First, the reaction mechanisms for the two half-cycles for the thermal ALE process are established. Electronically predicted geometries of the Al 2 O 3 structure with two precursors are optimized. Along with the optimized geometries, possible reaction pathways are proposed and calculated by density functional theory (DFT)-based electronic structure calculations. The proposed reaction paths and their kinetic parameters are used in a kinetic Monte Carlo (kMC) algorithm, which is capable of capturing the features of the thermal ALE of aluminum oxide. The kMC simulation provides an etch time for the given steady-state operating conditions, which are validated via comparison with available experimental results. Finally, data sets collected from the kMC simulation are used to train a feed-forward artificial neural network (FNN) model. The trained FNN model accurately predicts an etch time and dramatically reduces the computation time compared to the kMC simulation, thereby making it possible to carry out real-time, model-based operational parameter calculations. In addition, the trained FNN model can be used to establish a feasible range of operating conditions without demanding experimental work. [ABSTRACT FROM AUTHOR]

Increased globalization, deregulation of energy markets, and environmental constraints, together with associated uncertainty, have created a highly dynamic and uncertain process manufacturing environment. Responding effectively to this increased variation and uncertainty is critical for a company to remain competitive. In this paper, we consider the plant infrastructure in relation to the energy and global market infrastructures. We describe changes to the plant infrastructure system in order to function more effectively in the current manufacturing environment, as well as key research advances that are aligned to addressing challenges faced by present day plant operation. • Discusses relationship between plant, energy, and global market infrastructures. • Identifies challenges associated with the current plant operating environment. • Discusses industry response to identified challenges. • Overview of PSE tools and paradigms aligned with addressing these challenges. • PSE approaches illustrated in the context of industrial air separation plants. [ABSTRACT FROM AUTHOR]

• 3D CFD simulation of side-fired steam methane reformer was performed. • The numerical simulation was verified with domestic petrochemical plant data. • The effect of burner feed distribution on hydrogen production was studied. Simulation of the steam methane reformer serves as a launching pad to investigate the optimal amount of hydrogen production. In this study, a 3D-CFD simulation is implemented to model transport phenomena for a turbulent fluid flow with chemical reactions within the reformer. Accordingly, the realizable k-ɛ turbulence model and discrete ordinates (DO) radiation model have been employed to simulate the kinetic mechanism of the SMR process and hydrogen/methane combustion. In addition, the REV-scale approach is considered to model the flow in the porous catalyst bed. Numerical results show that 3D-CFD simulation can accurately model such complex flows in comparison to 2D simulation. In the following, 3D numerical simulations have been performed to investigate the effect of fuel distribution between burners on the amount of heat provided, which affects the amount of hydrogen production and the tube wall temperature. Results indicate that the burner feed flow without purge gas and vent gas streams has the highest efficiency in hydrogen production and leads to a ca. 10% increase in hydrogen production compared to industrial case. Although feeding the burner with purge gas and without vent gas increases hydrogen production by about 7% compared to industrial data, it may be a more suitable option due to lower natural gas consumption and economic savings. [ABSTRACT FROM AUTHOR]

Abstract: For distillation columns, a temperature is controlled to hold product quality while the column is operated at constant pressure. In practice, absolute stability of temperature and pressure is unachievable, whereas the product quality, namely, product composition, fluctuates with the variation of them. By analyzing phase equilibrium, the composition fluctuation, caused by slight temperature and pressure variation, cannot be ignored for precise distillation column. Thus, a novel approach is proposed to improve the performance of product quality control. For preliminary, the freestanding pressure dynamic model is established via calculating vapor molar holdup. The modeling method is applied to a commercial ethylene column, and the veracity and reliability of the dynamic model is improved. For principal part, the complementary control strategy is proposed on the basis of the improved dynamic behaviors. Besides qualitative analysis, 2 scenes that contain tight control for external disturbance and transition control for internal regulating are discussed to verify the feasibility and progressiveness of product quality control with complementary structure. [ABSTRACT FROM AUTHOR]

The concept of maintaining or enhancing chemical process safety encompasses a broad set of considerations which stem from management/company culture, operator procedures, and engineering designs, and are meant to prevent incidents at chemical plants. The features of a plant design that take action to prevent incidents on a moment-by-moment basis are the control system and the safety system (i.e., the alarm system, safety instrumented system, and safety relief system). Though the control and safety systems have a common goal in this regard, coordination between them has been minimal. One impediment to such an integrated control-safety system design is that the traditional industrial approach to safety focuses on root causes of incidents and on keeping individual measured variables within recommended ranges, rather than seeking to understand incidents from a more fundamental perspective as the result of the dynamic process state evolving to a value at which consequences to humans and the environment occur. This work reviews the state of the art in control system designs that incorporate explicit safety considerations in the sense that they have constraints designed to prevent the process state from taking values at which incidents can occur and in the sense that they are coordinated with the safety system. The intent of this tutorial is to unify recent developments in this area and to encourage further research by showcasing that the topic, though critical for safe operation of chemical processes particularly as we move to more tightly integrated and economics-focused operating strategies, is in its infancy and that many open questions remain. [ABSTRACT FROM AUTHOR]

Hydrogen is a required key material for petroleum refineries that convert crude oil into products with higher economic value and is often produced by the steam methane reforming (SMR) process, which synthesizes hydrogen and carbon oxides from methane and superheated steam in the presence of a nickel-based catalyst network in a steam methane reformer. To investigate methods for improving profits for a reformer while avoiding costly on-site parametric studies, a high-fidelity model of a steam methane reformer can be investigated. Motivated by this, the present work focuses on developing a computational fluid dynamics (CFD) model of an industrial-scale steam methane reformer that consists of 336 reforming reactors, 96 burners and 8 flue gas tunnels. The motivation for choosing the modeling strategies used in the industrial-scale steam methane reformer CFD model is discussed and is based on expected transport phenomena and chemical reactions within the reformer. Specifically, the finite rate/eddy dissipation turbulence-chemistry interaction model, global kinetic models of hydrogen/methane combustion, global kinetic model of the SMR process and standard k - ∊ turbulence model with the ANSYS Fluent enhanced wall treatment function are used to simulate the formation and consumption rates of all chemical components of the system. In addition, an empirical correlation for estimating the radiative properties of a homogeneous gas mixture, Kirchhoff’s law, Lambert Beer’s law and the discrete ordinate method are employed to simulate radiative heat transfer in the furnace side of an industrial-scale steam methane reformer. Moreover, the modeling strategies of the reforming tubes developed in our previous work are adopted to model 336 reforming tubes in the reformer. Subsequently, the boundary conditions (i.e., the reforming tube feed, burner feed and the energy leakage through the combustion chamber refractory wall) of the industrial-scale reformer CFD model are derived based on typical plant data. The simulation results produced by the industrial-scale reformer CFD model are shown to be in agreement with typical plant data reported in the SMR literature, with the simulation data generated by an industrial-scale reforming tube CFD model and with the simulation data generated by a reforming Gibbs reactor model, which validates the chosen modeling strategies and allows the CFD data to be considered to represent actual plant data with sufficient accuracy for use in industrial operating parameter studies. [ABSTRACT FROM AUTHOR]

It has been repeatedly suggested that the common cause-and-effect approach to evaluating process safety has deficiencies that could be addressed by a systems engineering perspective. A systems approach should consider safety as a system-wide property and thus would be required to integrate all aspects of the process involved with monitoring or manipulating the process dynamics, including the control, alarm, and emergency shut-down systems while operating them independently for redundancy. In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC design, the closed-loop state of a nonlinear process is guaranteed to enter the safety zone in finite time in the presence of uncertainty while maximizing a stage cost that reflects the economics of the process. Closed-loop stability is established for a nonlinear process under the proposed implementation strategy. [ABSTRACT FROM AUTHOR]

In this work, we introduce a furnace-balancing scheme that generates an optimal furnace-side feed distribution that has the potential to improve the thermal efficiency of a reformer. The furnace-balancing scheme is composed of four major components: data generation, model identification, a model-based furnace-balancing optimizer and a termination checker. Initially, a computational fluid dynamics (CFD) model of an industrial-scale reformer, developed in our previous work, is used for the data generation as the model has been confirmed to simulate the typical transport and chemical reaction phenomena observed during reformer operation, and the CFD simulation data is in good agreement with various sources in literature. Then, we propose a model identification process in which the algorithm is formulated based on the least squares regression method, basic knowledge of radiative heat transfer and the existing furnace-side flow pattern. Subsequently, we propose a model-based furnace-balancing optimizer that is formulated as an optimization problem within which the valve position distribution is the decision variable, and minimizing the sum of the weighted squared deviations of the outer reforming tube wall temperatures from a set-point value for all reforming tubes with a penalty term on the deviation of the valve positions from their fully open positions is the objective function. CFD simulation results provide evidence that the optimized furnace-side feed distribution created by the furnace-balancing scheme can reduce the severity of nonuniformity in the spatial distribution of furnace-side temperature in the combustion chamber even when the reformer is under the influence of common valve-related disturbances. [ABSTRACT FROM AUTHOR]

With the integration of computation, networking, and physical process components to seamlessly combine hardware and software resources to improve process efficiency, cybersecurity has become increasingly important for reliable process control, process operation, and supply chain management in the chemical process industries. This paper provides an overview of recent works on cybersecurity issues in the area of process control, process operation and supply chain. We start with an overview of recent cyber-attack detection and mitigation works via machine learning (ML) and model predictive control (MPC) to detect and handle intelligent cyber-attacks. Several most common intelligent cyber-attacks in industrial control systems are first presented, followed by machine learning detection methods and resilient control strategies with encryption–decryption tools to achieve secure communication in the sensor–controller and controller–actuator links. Novel control architectures with inherent robustness to prevent cyber-attacks are then presented. We continue with an overview of cybersecurity issues in process operations and supply chains as well as the interface between information technology and operational technology. Finally, we discuss recent efforts on the interface of cybersecurity and process safety and conclude with a discussion of open issues in this emerging research field. • Overview of cybersecurity issues in industry. • Information technology versus operational technology cybersecurity concerns. • Elaboration on the nature and type of cyberattacks. • Review of research results on cyberattack detection and mitigation. [ABSTRACT FROM AUTHOR]

A novel short-term planning model of the ethylene plant that incorporates the operating variables and energy utilization in both the thermal cracking and the down-stream process is proposed to explore the potential for increasing the production margin and reducing the energy losses. A multi-period mixed-integer nonlinear programming (MINLP) model is formulated to attain the scheduling of parallel furnaces and the energy distribution of the overall plant, along with the determination of the key process operation involving the coil outlet temperature (COT) and coke deposition. The behavior of the product yields and coke formation in terms of varying COT profiles is investigated to enhance the profitability of the whole plant. A real industrial example is investigated to exploit the performance of the proposed model. The results show that the integrated approach attains an improvement in overall profit and achieves significant enhancement in energy savings, compared with the original optimization approach. [ABSTRACT FROM AUTHOR]

Grinding circuit (GC) is the most critical production unit and it also has the highest energy consumption in mineral processing operations. The control and optimization of GC are regarded as important ways to improve product quality and production efficiency of the whole concentration process. The fundamental goal of the automation system of GC is to make the outputs of the controlled processes best follow the control set-points. Moreover, from the standpoint of process engineering, it should as well ensure that the grinding product quality and efficiency during production phase are well controlled within the optimal ranges. Those goals cannot be achieved solely at the level of basic feedback control where global operational indices are not considered. Therefore, higher-level advanced control is required for the whole grinding plant operation to achieve integrated control and optimization of the indices of control, operation and mineralogical economics. This paper overviews the available advanced control methods and technologies for improving operation of GC system based on our experiences of research and practice in this field. A brief introduction of GC and its advanced control problem for process operation are presented first. Then, a comprehensive and systematic review on the available methods and technologies of higher-level grinding advanced control is given. The emphasis of this review is on the approach of data & knowledge based hybrid intelligent advanced feedback control. Issues about the future research on the advanced control of GC are outlined when concluding the paper. [ABSTRACT FROM AUTHOR]

• Multiscale model of spatial thermal atomic layer etching. • Computational fluid dynamics modeling of the gas phase. • Kinetic Monte-Carlo modeling of etching process. • Evaluation of process designs and operating conditions. Spatial atomic layer deposition and etching processes have emerged to reduce the overall process time while maintaining the conformity of thin films by functioning continuously. This research constructs an multiscale computational fluid dynamics (CFD) model with a dynamic mesh that combines a microscopic kinetic Monte Carlo algorithm of the film etching with a CFD simulation of the gas phase for the spatial thermal atomic layer etching of Al 2 O 3. Using this model, we evaluate the effect that design and operation variables including the gap distance, purge and precursor gas flow rates, substrate velocity, and vacuum pressure have on the substrate film etching per cycle and uniformity. Numerical results suggest that small gap distances with sufficiently high N 2 flow are desired to accomplish effective precursor separation for reactor configuration and the process operation requires low substrate velocities and low vacuum pressures to achieve optimal film quality and conformance. [ABSTRACT FROM AUTHOR]

Abstract: Expanded perlite is a lightweight material with remarkable thermal and acoustic insulation properties, rendering it widely useful in the construction and manufacturing industries. Currently applied perlite expansion technology suffers numerous technical disadvantages, which adversely affect product quality and limit the range of its applications. To overcome these established drawbacks, a new perlite expansion process has been designed on the basis of a vertical electrically heated expansion furnace. The novel furnace enables precise control of experimental conditions, in order to allow for efficient adjustment of particle residence time and internal temperature. The quality of expanded perlite strongly depends on raw material thermophysical properties as well as furnace operating conditions, and the experimental investigation of the isolated effect of each parameter on expanded product quality is technically cumbersome and extremely time-consuming and expensive. A mathematical model for perlite grain expansion has been developed in order to perform a detailed numerical investigation of process efficiency, toward the optimization of the expansion process in the novel pilot-scale furnace. The dynamic model consists of ordinary differential equations for both air and particle heat and momentum balances, as well as nonlinear algebraic equations for both air and perlite melt thermophysical and transport properties, probing the air temperature distribution within the vertical electrical furnace as well as the particle velocity, temperature and size along its trajectory inside the heating chamber. The effect of raw material physical properties (raw feed origin, initial particle size, effective water content) as well as operating parameters (air inlet temperature and flowrate, furnace wall temperature) on evolution of the particle state variables is presented and discussed. Model results indicate perlite expansion is strongly affected by raw ore feed origin, size and water content. Moreover, operating conditions affect expansion considerably, and furnace wall temperature has the strongest effect on the final particle expansion ratio attained. The new dynamic model is instrumental towards achieving a detailed comprehension of perlite expansion in the vertical electrical furnace towards multi-parametric sensitivity analysis, process optimization and efficient control. [Copyright &y& Elsevier]

[Display omitted] • Evaluation of semi-supervised regression for application in (bio-) chemical processes. • Construction of soft-sensors for systems with many continuous measurements and a few, vital rarely measured qualities. • Highly reliable prediction of steady-states despite presence of noise and larger disruptions. Continuously operated (bio-) chemical processes increasingly suffer from external disturbances, such as feed fluctuations or changes in market conditions. Product quality often hinges on control of rarely measured concentrations, which are expensive to measure. Semi-supervised regression is a possible building block and method from machine learning to construct soft-sensors for such infrequently measured states. Using two case studies, i.e., the Williams-Otto process and a bioethanol production process, semi-supervised regression is compared against standard regression to evaluate its merits and its possible scope of application for process control in the (bio-) chemical industry. The case studies show that semi-supervised regression can serve as a valuable building block in construction of a soft sensor to reliably predict steady state data even in case of very few quality measurements (hourly or daily measurements). However, no reliable prediction of process dynamics can be ensured in case of common measurement frequencies for offline quality measurements. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the cost assessment of different hydrogen peroxide addition strategies with the assistance of dissolved oxygen on-line monitoring as the controlled variable. In this way, manual, automatic and controlled hydrogen peroxide supplies were assayed. Although the application of the photo-Fenton process to remove persistent pollutants in wastewater is accepted, there are few studies linking existing scientific and economic knowledge to evaluate further improvements in the decontamination processes. This economic study relates total costs to water treatment capacity and mineralization for a 100m2 CPC plant. Results showed that the amount of H2O2 added and the treatment time are decisive in cost reduction, dissolved oxygen monitoring being a key tool in predicting both variables. Furthermore, for low mineralization values, the addition strategy is not significant in financial terms, obtaining a mean cost of 0.53€/m3 for 50–60% mineralization. However, for higher mineralization levels, the cost is quite sensitive to mineralization changes and, so, Proportional and Integral controlled hydrogen peroxide dosing would be a better option. [Copyright &y& Elsevier]

Abstract: According to the mechanism of the reaction operation involved, reactive distillation columns are often designed to work in a totally refluxed operation mode. The totally refluxed operation mode makes the reflux drum interact solely with the reaction operation involved and retards considerably the dynamics of the latter. The resultant great difference in process dynamics between the reaction operation and the separation operation involved leads frequently to under-damped responses with the degree of under-dampness closely dependent on the inventory control of the reflux drum. With the tight inventory control of the reflux drum, the degree of under-dampness can be suppressed and this presents a favorable effect to process dynamics and controllability of the totally refluxed reactive distillation columns. Two hypothetical ideal reactive distillation columns with and without a side reaction, respectively, and a high-purity ethylene glycol reactive distillation column are employed to examine the unique dynamics and controllability of the totally refluxed reactive distillation columns. The results obtained are in good accordance with the above interpretation. The current work reveals the general behaviors of the totally refluxed reactive distillation columns and can be particularly useful in control system synthesis and design. [Copyright &y& Elsevier]

Abstract: In the first three papers of this series, it has been shown that strengthening internal heat integration within a reactive distillation column involving reactions with high thermal effect is really effective for the reduction of utility consumption and capital investment besides the improvement in process dynamics and operation. One important issue that remains unstudied so far is the influences of reaction selectivity upon the reinforcement of internal heat integration, since the reaction operation is often accompanied by side-reactions and the maintenance of a high selectivity is extremely necessary in process synthesis and design. A reactive distillation column synthesizing high-purity ethylene glycol through the hydration of ethylene oxide is chosen and studied in this work. Because of the unfavorable physicochemical properties of the reacting mixture separated (e.g., the fairly large volatility between the reactants and the existence of a consecutive side-reaction), the process represents a challenging problem for the reinforcement of internal heat integration. Intensive comparison is conducted between the process designs with and without the consideration of further internal heat integration between the reaction operation and the separation operation involved, and it has been found that seeking further internal heat integration still leads to a substantial reduction of energy requirement, in addition to a further abatement in capital investment. Moreover, a favorable effect is again observed upon the process dynamics and operability. These striking outcomes manifest evidently that seeking further internal heat integration should be considered in process synthesis and design irrespective of what a reaction selectivity has been assigned. [Copyright &y& Elsevier]

Abstract: Although heap leaching has become established as the technology for treatment of some copper, gold, and zinc minerals, as well as its extension to the treatment of other types of minerals such as saltpeter and mine tailings, little study has been made on the optimization of this technology. Usually the operation of heap leaching is carried out until the maximum recovery has been obtained, or until observing that the concentration in the output solution of the heap exhibits no further extraction. The present study makes an analysis to determine if these criteria present the best conditions from the economic standpoint. Two variables are examined including, (1) leaching time, and (2) height of the heap, at a copper mineral treatment plant. The results of the study showed that the design (height of the heap), and planning of the operation (operational time) were interactive factors, and that maximum recovery was not necessarily the best measure of operational efficiency based on economic considerations. [Copyright &y& Elsevier]

Abstract: Despite the fact that a stand-alone ideal heat-integrated distillation column (ideal HIDiC) can be thermodynamically efficient and operationally stable, the application of an ideal HIDiC system to separate a close-boiling multi-component mixture is still a challenging problem because of the possibility of strong interactions within/between the ideal HIDiCs involved. In this work, employment of two ideal HIDiCs to separate a close-boiling ternary mixture is studied in terms of static and dynamic performance. It is found that the ideal HIDiC system can be a competitive alternative with a substantial energy saving and comparable dynamic performance in comparison with its conventional counterpart. The direct sequence appears to be superior to the indirect sequence due to the relatively small vapor flow rates to the compressors. Controlling the bottom composition of the first ideal HIDiC with the pressure elevation from the stripping section to the rectifying section helps to suppress the disturbances from the feed to the second ideal HIDiC. Special caution should, however, be taken when the latent heat of the distillates is to be recovered within/between the ideal HIDiCs involved, because a positive feedback mechanism may be formed and give rise to additional difficulties in process operation. [Copyright &y& Elsevier]

Abstract: Process intensification calls for the combination of two or more conventional unit operations within one framework, thus giving rise to a challenging coordination problem in process synthesis and design. Seeking synergistic effect among all the conventional unit operations involved is found to be an important principle for process development. Three cases of process intensification are studied to evaluate the principle, including: (i) a hypothetical reactive distillation system; (ii) a reactive distillation column for the hydration of ethylene oxide; (iii) an ideal heat-integrated distillation column. It is demonstrated that simply combining two or more conventional unit operations together does not necessarily tap out the full potentials of capital investment reduction and energy saving. Only after the synergistic effect has been deliberately considered in process synthesis and design, can process intensification be carried out in the most effective manner. Furthermore, seeking synergistic effect appears frequently to yield improved process dynamics and provide additional redundancy to process operation because the underlying conflicts could be attenuated among all the conventional unit operations involved. [Copyright &y& Elsevier]

Presented in this paper is an integrated approach of computer-aided product development, process design and operation analysis based on a kilo-plant. The implemented kilo-plant, as a research platform to manufacture product in kilogram-scale, was designed especially for fine and specialty chemicals. The characteristics of product synthesis, process operation and product quality control are investigated coupled with computer-aided monitoring, online modeling, simulation and operation process optimization. In this way, chemical product discovery, process design and operation are integrated in a systematic approach, in the aim to respond to rapid changing market-place demands to new products. [ABSTRACT FROM AUTHOR]

Abstract: A two-parameter model, termed as a two-MSMPR-crystallizers-with-interchange model, is proposed in this paper to predict the overall performances of a nonideal, continuous-stirred crystallizer. The model equations, including the population balance equations and the mass balance equations, are solved simultaneously to predict the resulting crystal size distribution (CSD) for power law growth and nucleation kinetics. The effects of nonhomogeneous mixing, in terms of the two parameters, α and β, on the weight-average size, the coefficient of variation and the production rate of the resulting crystals are studied. [Copyright &y& Elsevier]

Abstract: Classification of multivariate, multi-class, temporal patterns is an important yet challenging problem that arises during state identification in agile processes. In this paper, we propose two new neural network structures that overcome the curse of dimensionality and the complexities in temporal pattern recognition. The one-variable-one-network (OVON) architecture decomposes the problem in the variable dimension—univariate temporal patterns, called sub-states, are first identified in each variable; subsequently, the process state is inferred using the variable sub-states. The one-class-one-network (OCON) architecture uses a problem decomposition in the class dimension—the presence of specific temporal patterns in a high-dimensional space is first established using a bank of two-class classifiers. The process state is then identified by aggregating the results of the different classifiers. Both the architectures use a set of neural networks—in OVON there is one network for each variable, while in OCON, one network is used for each pattern class to be identified. In comparison to traditional monolithic neural networks, both the proposed architectures improve classification accuracy and minimize the training complexity. In addition, OVON is robust to sensor failures and OCON is well suited for addition of new pattern classes. The structures and training methodologies of the two architectures are presented and their performance compared against traditional neural networks using patterns arising during transitions in a simulated fluidized catalytic cracking unit. [Copyright &y& Elsevier]

Multivariate statistics (MVS) has enjoyed popularity in the applied science literature over the last decade. It has also been well received by industrial practitioners; however, industrial applications have had mixed results. In this paper, we focus on our experiences in developing multivariate statistical systems for industrial use. From these experiences, we identify a methodology for developing useful, long-standing industrial applications. We highlight features we feel are important in the successful development of on-line MVS applications, both technical and non-technical. Specifically, we focus on on-line systems for manufacturing environments. Many of the applications discussed grew out of industry-university collaborations. To end the paper, we recommend topics open for further academic research with an industrial focus. [Copyright &y& Elsevier]

This article addresses the detection of oscillations in measurements from chemical processes including the case when two or more oscillations of different frequency are present simultaneously. The presence of oscillations in selected frequency ranges is determined using a new method based on the regularity of the zero crossings of filtered autocovariance functions. The work is motivated by and illustrated with industrial data that exhibit multiple plant-wide oscillations. Issues of practical implementation in an automated tool are discussed. [Copyright &y& Elsevier]

This article describes principal component analysis (PCA) of the power spectra of data from chemical processes. Spectral PCA can be applied to the measurements from a whole unit or plant because spectra are invariant to the phase lags caused by time delays and process dynamics. The same comment applies to PCA using autocovariance functions, which was also studied. Two case studies are presented. One was derived from simulation of a pulp process. The second was from a refinery involving 37 tags. In both cases, PCA clusters were observed which were characterised by distinct spectral features. Spectral PCA was compared with PCA using autocovariance functions. The performance was similar, and both offered an improvement over PCA using the time domain signals even when time shifting was used to align the phases. [Copyright &y& Elsevier]

Biomass with a large amount of moisture is well-suited to be processed by supercritical water gasification, SCWG. The precipitation of inorganics, together with char formation and re-polymerization, can cause reactor plugging and stop the process operations. When plugging occurs, sudden injections of relatively large mass quantities take place, influencing the mass flow dynamics significantly in the process. Reactor plugging is a phenomenon very well observed during SCWG of industrial feedstock, which hinders scale-up initiatives, and it is seldom studied with precision in the literature. The present study provides an accurate evaluation of continuous tubular reactor dynamics in the event of sudden injections of water. An interpretation of the results regarding water properties at supercritical conditions contributes to comprehending mass and heat transfer when plugging occurs. Experiments are then compared to SCWG of a biomass sample aiming to give key insights into heat transfer and fluid dynamics mechanisms that could help develop operational and control strategies to increase the reliability of SCWG. In addition, a simplified model is presented to assess the effect of material integrity on burst-event likelihood, which states that SCWG is safe to operate, at 250 bar and 610 °C, in tubular reactors made of 0.22 wall thickness-to-diameter ratio Inconel-625 with superficial microfractures smaller than 30 µm. We also suggest improvement opportunities for the safety of SCWG in continuous operation mode. [ABSTRACT FROM AUTHOR]