Continuous Industrial Processes Research Articles

Challenges in the Synthesis and Processing of Hydrosilanes as Precursors for Silicon Deposition.

Liquid hydrosilanes are required for the production of silicon films. The silicon layers can be processed for electronic devices like transistors or thin-film solar cells. Hydrosilanes are highly reactive and pyrophoric. Therefore, the synthesis of these compounds is challenging and dangerous. The available synthesis methods for hydrosilanes are reviewed and compared. Hydrosilanes are highly attractive compounds, which can be processed as liquids with printing technology to amorphous silicon films on nearly any solid substrate. The silicon layers can be processed for electronic devices like transistors or thin-film solar cells. The endothermic character of hydrosilanes with their positive enthalpies of formation results in favorable properties for processing. The larger the molecules, the lower their decomposition temperature and the higher their photoactivity. Cyclic hydrosilanes such as cyclopentasilane and cyclohexasilane can be easily deposited. The branched neopentasilane is more difficult to deposit but yields better-quality films after processing. The key challenge is the complex synthesis of the precursors and the hydrosilanes. The available preparative methods are presented in this review and their advantages and disadvantages are evaluated. The following synthesis methods are presented and discussed in this article: Wurtz coupling and other reductive coupling processes, dehydrogenative coupling of silanes, plasma synthesis of chlorinated polysilanes, amine- or chloride-induced disproportionations, and transformation of monosilane to higher silanes. Plasma synthesis is already carried out today as a continuous industrial process. The most effective synthesis methods in the laboratory are currently amine- and chloride-induced disproportionations. There is a great need to further optimize the syntheses of hydrosilanes and to develop new simple synthesis variants.

Hydrodynamic features of pulsed solvent extractor for separation of two metals by using the antagonistic effect of solvents

Separating copper and cobalt ions is crucial due to the industry’s strategic reliance on both these elements. When the extraction process is able to significantly increase the separation factor, it becomes favorable to separate two ions. However, the presence of Cu(II) ions together with Co(II) hinders the achievement of optimum efficiency when using commonly available extractants. This study conducted the separation of the two elements using both batch and continuous methods in a pilot plant pulsed column equipped with a disc and doughnut structure. The initial step involved optimizing the key variables to maximize the separation factor using the central composite design procedure. The optimization of Cyanex272, Cyphos IL 101 concentrations, and the pH value of the aqueous phase were all adjusted to 0.024 M, 0.046 M, and 7.3, correspondingly. In the following step, the hydrodynamic characteristics and extraction performance were examined in the pulsed column of the pilot plant. The findings indicated that the presence of Cyphos IL 101 resulted in an increased separation factor and efficiency within the column. As a result, the ionic liquid enhances performance without encountering any operational issues. This additive is considered an environmentally friendly solvent and does not cause any negative impacts. Consequently, it is suggested for utilization in continuous industrial processes.

Variable time delay estimation in continuous industrial processes

Digital sensors and machine learning enable efficiency improvements in production processes, through process monitoring, anomaly detection, soft sensing, and process control. However, the development of such solutions requires several data preprocessing steps. In continuous processes, a crucial part of the data preparation is adjusting for time delays between different sensors. This is necessary to ensure that each sensor measurement relate to the same volume of materials going through various processing steps.This study provides an overview of data-driven methods for estimating time lags between sensors in continuous processes. The methods are assessed in a large simulation study, on data sets with different sample sizes, model complexities and autocorrelation functions. Our results shows that most methods work well if the relationships are close to linear, but more flexible metrics like distance correlation and maximum information coefficient are needed in more complex systems. Finally, we present a real industrial example to illustrate some real-world aspects of the variable time delay estimation process.

Development of a New Type of Electrochemical Reactor for Low Temperature Lime and Cement Production

The cement industry is responsible for more than 8% of the global CO2 emissions, making it one of the largest contributors to global warming. Two-thirds of these emissions are coming from unavoidable process gases caused by the calcination reaction of limestone (CaCO3). The remaining emissions are mainly coming from the combustion of fossil fuels. Due to the high temperature requirement (≈1500°C), direct electrification is difficult to consider. For these two reasons, the cement industry is therefore known as a challenging sector to decarbonize.A new electrochemical pathway for cement production has gained prominence in the scientific literature in recent years [1]. This technology has the potential to electrify the cement and lime production while producing at the same time valuable gases (O2, CO2, H2). It is based on a new type of hybrid electrolyser capable of working with solid, liquid and gaseous phases. Thanks to the acidity generated by water oxidation at the anode, the solid limestone feed, precursor of cement, is being dissolved. This dissolution produces pure CO2 which is mixed with pure O2 generated by electrolysis and which is easily captured for later use. The calcium ions are then migrating towards the cathode under the effect of the electric field. The OH- hydroxyl ions, produced at the cathode by water reduction, meet the calcium ions to form the final solid product, hydrated lime (Ca(OH)2). This lime can be used as such or converted into cement by additional steps.In this research, a laboratory-scale setup has been developed to study the feasibility of such a process. Different geometries have been tested and the main operational parameters affecting the different steps of the process have been investigated. Our results show first of all the formation and stabilization of a pH gradient within the electrolyser, which allows at the same time for the complete dissolution of CaCO3 in the acidic anodic environment while producing a white precipitate of Ca(OH)2 at the cathode. The setup also presents ideal faradaic efficiencies when using an IrO2 coated titanium anode and a nickel cathode in a 1M NaClO4 electrolyte. Alternative electrode materials, including a 316L stainless steel anode and an Inconel 718 cathode, have been considered as well. Finally, it was shown that the energetic efficiency can be further improved by pushing the kinetics of the dissolution and precipitation reactions through increasing the mobility of calcium ions, using Ca-based electrolytes, like Ca(ClO4)2. Based on our results, all the elementary concepts behind the hybrid electrolyser have been established, and a first blueprint of a continuous industrial process will be proposed.[1] - L. D. Ellis, et al. (2019). Toward electrochemical synthesis of cement—An electrolyzer-based process for decarbonating CaCO3 while producing useful gas streams. PNAS , 117, 12584-12591. Figure 1

Effect of chemical composition and isothermal treatment time on the microstructure and mechanical properties of high-strength carbide-free bainitic steel

Recent works about the development of carbide-free bainitic (CFB) steels use chemical compositions that require too long isothermal treatments for the bainitic transformation, making it difficult their manufacture under industrial continuous annealing processes. The present work reports an approach for designing CFB steels based on a computer simulation of phase transformations as a function of chemistry. The effect of chemistry and isothermal bainitic treatment (IBT) time on the microstructure and mechanical properties of CFB steels are also investigated. Results show that CFB steels of third generation, containing about 96% of bainite, can be obtained from short IBT times, opening the possibility to obtain these steels under the said conditions.

Influence of casting speed change and submerged entry nozzle offset on mold flow field in unsteady-state continuous casting process

For the unsteady-state continuous casting process, the influences of changing the casting speed and the submerged entry nozzle (SEN) offset on the mold flow field are investigated with the water model experiment. The process of changing casting speed under the unsteady-state condition was divided into three phases to study the variation of the flow field in the mold, namely, the phase at the beginning of increasing or decreasing the casting speed, the phase of continuous increasing or decreasing the casting speed and the phase at the end of increasing or decreasing the casting speed. From the view point of the volumetric flow rate of the molten steel and the number of bubbles, the mechanisms for the variation of the flow field in the mold are discussed. In the industrial continuous casting process, since changing the casting speed has the greatest influence on the surface velocity and level fluctuation at the larger casting speed (1.6 m/min), the change in the casting speed should be operated more slowly at the larger casting speed. In addition, the variation of the flow field in the mold is studied when the SEN is offset to front, back, left and right on the cross section, and the mechanisms for the variation of the flow field in the mold are discussed from the view point of the number of bubbles and the kinetic energy of the jet.

Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model

Hidden Markov models (HMMs) have been recently used for fault detection and prediction in continuous industrial processes; however, the expected maximum (EM) algorithm in the HMM has local optimality problems and cannot accurately find the fault root cause variables in complex industrial processes with high-dimensional data and strong variable coupling. To alleviate this problem, a hidden Markov model-Bayesian network (HMM-BN) hybrid model is proposed to alleviate the local optimum problem in the EM algorithm and diagnose the fault root cause variable. Firstly, the model introduces expert empirical knowledge for constructing BN to accurately diagnose the fault root cause variable. Then, the EM algorithm is improved by sequential and parallel learning to alleviate the initial sensitivity and local optimum problems. Finally, the log-likelihood estimates (LL) calculated by the improved hidden Markov model provide empirical evidence for the BN and give fault detection, prediction, and root cause variable detection results based on information about the similar increasing and decreasing patterns of LL for the training data and the online data. Combining the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process, the feasibility and effectiveness of the model are verified. The results show that the model can not only find the fault in time but also find the cause of the fault accurately.

Ultrafast Crystallization and Sintering of Li1.5Al0.5Ge1.5(PO4)3 Glass and Its Impact on Ion Conduction

Li1.5Al0.5Ge1.5(PO4)3 (LAGP) is among the most promising solid electrolytes for the next generation’s all-solid-state lithium batteries. However, preparing LAGP electrolytes is time- and energy-intensive. In this work, LAGP glassy powders were sintered and crystallized in 180 s by ultrafast high-temperature sintering (UHS) under conditions attractive for continuous industrial processes (i.e., ambient pressure and atmosphere). The fast heating rates characteristic of UHS significantly delay crystallization, potentially decoupling crystallization and sintering. Furthermore, electrochemical impedance spectroscopy (EIS) characterizations reveal that LAGP sintered and crystallized by UHS has an ionic conductivity of 1.15 × 10–4 S/cm, slightly lower than conventionally annealed samples (1.75 × 10–4 S/cm). The lower conductivity can be attributed to poorer intergrain contact. To overcome this issue, additives such as B2O3 and Li3BO3 are used, resulting in ∼2 and ∼5 times higher grain boundary conductivity for LAGP+1 wt % B2O3 and LAGP+1 wt % Li3BO3, respectively, compared to LAGP. Overall, this work provides insights into unraveling the impact of UHS sintering on the LAGP Li+ conduction mechanism.

Pd Nanoparticle-Loaded Smart Microgel-Based Membranes as Reusable Catalysts.

In this work, palladium-loaded smart membranes made by UV cross-linking of thermoresponsive microgels are prepared to obtain a reusable, catalytically active material which can, for example, be implemented in chemical reactors. The membranes are examined with respect to their coverage of a supporting mesh via atomic force microscopy measurements. Force indentation mapping was performed in the dried, collapsed state and in the swollen state in water to determine the Young modulus. Furthermore, we compare the catalytic activity of the membrane with the corresponding suspended colloidal nanoparticle microgel hybrids. For this purpose, the reduction of 4-nitrophenol is an established model reaction to quantify the catalytic activity by UV-vis spectroscopy. The membrane is embedded inside a continuous stirred tank reactor equipped for continuous monitoring of the reaction progress. Although catalysis with membranes shows lower catalytic activity than freely dispersed particles, membranes allow straightforward separation and recycling of the catalyst. The fabricated membranes in this work show no decrease in catalytic activity between several cycles, unlike free particles. The feasible and durable deposition of catalytically active inter-cross-linked microgel particles on commercial nylon meshes as supporting scaffolds, as demonstrated in this work, is promising for up-scaling of continuous industrial processes.

Deep learning-based on-line image analysis for continuous industrial crystallization processes

In situ microscopic imaging is a useful tool in monitoring crystallization processes, including crystal nucleation, growth, aggregation and breakage, as well as possible polymorphic transition. To convert the qualitative information to be quantitative for the purpose of process optimization and control, accurate analysis of crystal images is essential. However, the accuracy of image segmentation with traditional methods is largely affected by many factors, including solid concentration and image quality. In this study, the deep learning technique using mask region-based convolutional neural network (Mask R-CNN) is investigated for the analysis of on-line images from an industrial crystallizer of 10 m3 operated in continuous mode with high solid concentration and overlapped particles. With detailed label points for each crystal and transfer learning technique, two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data amount. The former model effectively segments the aggregated and overlapped crystals even at high solid concentrations. Moreover, it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall, revealing the importance of large number of crystals in deep learning. Some geometrical characteristics of segmented crystals are also analyzed, involving equivalent diameter, circularity, and aspect ratio.

Evolutionary neural architecture search for surrogate models to enable optimization of industrial continuous crystallization process

Optimal performance of the crystallization process is of utmost importance for industries handling bulk commodity chemicals to pharmaceuticals. Such an optimization exercise becomes extremely time expensive as the mathematical models mimicking such complex processes involve the solution of Integro-Differential Population Balance Equations using High Resolution Finite Volume Methods. In order to build a fast and robust data based alternative model, a surrogate assisted approach using Artificial Neural Networks has been proposed here. To overcome the heuristics-based estimation of the hyper-parameters in ANNs, we aim to contribute a novel Neural Architecture Search strategy for the auto-tuning of hyper-parameters integrated with sample size determination techniques. While solving a multi-objective optimization of crystallization process ensuring maximum productivity, the results from surrogates are compared with those of a high-fidelity physics driven model, which reports five order of magnitude speed improvement without sacrificing much on accuracy.

Low-Cost Potentiometric Sensor for Chloride Measurement in Continuous Industrial Process Control.

Recently, the new updates in legislation about drinking water control and human health have increased the demand for novel electrochemical low-cost sensors, such as potentiometric ones. Nowadays, the determination of chloride ion in aqueous solutions has attracted great attention in several fields, from industrial processes to drinking water control. Indeed, chloride plays a crucial role in corrosion, also influencing the final taste of beverages, especially coffee. The main goal is to obtain devices suitable for continuous and real-time analysis. For these reasons, we investigated the possibility to develop an easy, low-cost potentiometric chloride sensor, able to perform analysis in aqueous mediums for long immersion time and reducing the need of periodic calibration. We realized a chloride ion selective electrode made of Ag/AgCl sintered pellet and we tested its response in model solutions compatible with drinking water. The sensor was able to produce a stable, reproducible, and accurate quantification of chloride in 900 s, without the need for a preliminary calibration test. This opens the route to potential applications of this sensor in continuous, in situ, and real time measurement of chloride ions in industrial processes, with a reduced need for periodic maintenance.

Monitoring of total acidity content in continuous vinegar fermentation using a self-made modification of a micro-electro-mechanical system near-infrared spectrometer

This study evaluated the potential of a micro-electro-mechanical system (MEMS) near-infrared spectrometer for the on-line monitoring of total acids in the continuous fermentation process of rice vinegar and white vinegar. A self-made modification of the digital light processing NIRscan nano spectrometer was used for this purpose. As best pretreatment method, the multiplicative scatter correction was used to pretreat the raw spectra, which were subsequently subjected to a partial least square regression calibration. The results showed that total acids could be determined by this experimental setup with root-mean-square errors of predictions of 0.12% (w/v) and 0.27% (w/v) for rice vinegar and white vinegar, respectively. In conclusion, this low-cost MEMS near-infrared instrument can be applied to monitor total acids in the industrial continuous fermentation process of rice and white vinegar.

Electrocoagulation technique for continuous industrial licorice processing wastewater treatment in a single reactor employing Fe-rod electrodes: Process modeling and optimization and operating cost analysis

Licorice processing industry produces a characteristic wastewater with a high chemical oxygen demand (COD), high turbidity, and intense color. Direct disposal of untreated licorice processing wastewater into water bodies can pose a serious risk to the human health and environment. The main objective of this study is to evaluate the performance of a novel laboratory scale continuous electrocoagulation (EC) reactor using iron (Fe) rod electrodes (anode and cathode) for treating a real licorice processing wastewater. Response surface methodology (RSM) was employed to investigate the effects of important parameters such as: current density (CD), electrolysis time, mixing intensity, and NaCl concentration for Fe rod electrodes on the removal efficiency of color, soluble COD (sCOD), and turbidity. In order to evaluate the effect of reactor design on the overall process performance, the results were compared with our previous study in which Fe plate electrodes were employed in an identical system. The process exhibited better performance in terms of turbidity removal for Fe rod electrodes with an average value of 59.35% in contrast to Fe plate electrodes (47.88%). Color and sCOD removal efficiencies of 94.6% and 90.1% were achieved, respectively for the EC system using Fe rod electrodes under optimum conditions: electrolysis time (71.8 min), CD (28 mA/cm2), mixing intensity (45 rpm). While in the EC reactor using Fe plate electrodes, the optimum (electrolysis time of 81.8 min, current density of 35 A/cm2, and mixing intensity of 45 rpm) color and sCOD removal were obtained 90.1% and 89.4%, respectively. Analysis and comparison of the data revealed that Fe rod electrodes delivered a better treatment performance than Fe plate electrodes, taking into account that the optimum electrolysis time and applied current density were considerably lower for the Fe rod electrodes. Overall, these results confirmed that the novel EC reactor using Fe rod electrodes can reduce the operating cost and be proposed as a pragmatic approach to remove high amount of color, COD, and suspended solids from licorice processing wastewater.

Orange-derived and lemon-derived adsorbents with controlled grain for an efficient elimination of some cationic and anionic dyes

Purpose This study aims to demonstrate that orange-derived and lemon-derived systems can be used in continuous processes as efficient adsorbents to the entrapment of some anionic and cationic dyes in the textile dyeing wastewater effluents. Design/methodology/approach Physically and chemically modified orange and lemon mesocarps are used as natural adsorbents for the cationic dyes Basic Blue 3, Basic Yellow 21, Basic Red 18 and Basic Green 4 and the anionic dyes Acid Blue 264, Acid Yellow 49 and Acid Red 337, all commonly used in the textile dyeing industry. Adsorption capacities of the orange-derived and lemon-derived adsorbents on the dyes are studied simulating a batch and continuous industrial processes. Findings Results demonstrate that treated orange mesocarp (orange-derived adsorbent) can adsorb up to 97% of cationic Basic Green 4 in 30 min, whereas the lemon mesocarp (lemon-derived adsorbent) can retain up to 88% within the same time. In the case of anionic, 91% Acid Blue 264 is adsorbed by the orange mesocarp in 15 min, whereas 92% is adsorbed by the lemon homologue within the same time. Originality/value As far as the authors know, physically and chemically modified orange and lemon mesocarps have not been used on the removal of cationic (Basic Blue 3, Basic Yellow 21, Basic Red 18 and Basic Green 4) and anioinic (Acid Blue 264, Acid Yellow 49 and Acid Red 337) dyes of textile dyeing wastewater industry. It is a costless and efficient treatment that supposes, on the one hand, an eco-friendly and feasible process for discolouration of wastewater and, on the other, a valorisation (upcycling) of orange and lemon peels, which are not currently used.

Model development and control of an auto-refrigerated polystyrene polymerization reactor

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

Progress in spray-drying of protein pharmaceuticals: Literature analysis of trends in formulation and process attributes

Spray-drying is an inherently continuous and well-established industrial drying process. It can accelerate manufacturing of biopharmaceuticals and vaccine products, resulting in both an economic and health benefit. In this review, we cover a systematic assessment and discuss the spray-drying of diverse protein pharmaceuticals and excipients included therein, solvent systems applicable to these formulations, equipment used and, respective process parameters. Further, key quality aspects of spray-dried protein solids are discussed. Based on the overall trends, we present a concise perspective into the future of protein pharmaceuticals spray-drying.

Effect of drying method and process conditions on physicochemical and rheological properties of arabinoxylans extracted from corn-lime-cooking-liquor on a pilot plant scale

Corn-lime-cooking-liquor (nejayote), which is a pollutant wastewater from the corn-tortilla-industry, contains valuable products, such as arabinoxylans (AXs) and phenolics. Recently, an innovative process that employs enzymatic and ultrafiltration processes for recovering and purifying AXs from nejayote was described, although neither the drying methods nor the physicochemical and rheological properties of the recovered AXs by this method at the pilot plant scale have been studied. Nejayote from a batch artisanal process (NBAP) and from a continuous industrial process (NCIP) were compared with a corn-bran-hydrolysate (CBH) as sources for AXs obtention. Freeze drying (FD), spray drying (SD) and solvent drying (SoD) were assessed as processes for drying purified and concentrated AXs solutions. Regardless of the process, AXs obtained from nejayote contain 3 times less (0.30 mg/g) esterified ferulic acid (EFA) than did AXs from corn bran (1.01 mg/g) and thus fail to yield elastic gels upon oxidative cross-linking. Molecular weight distribution (MwD) and dynamic viscosity data show that AXs from nejayote are shorter than AXs from corn bran. In general, the drying method had no effect on the physicochemical and rheological properties of AXs, although additional purification using ethanol precipitation and SoD, eliminated low molecular weight oligosaccharides and residual salts resulting from the neutralization of the corn-lime-cooking-liquor. Spray drying seems to be the most cost-effective unit operation for the industrial production of AXs from nejayote without affecting their physicochemical characteristics.

The Significance of Optimizing Mn-Content in Tuning the Microstructure and Mechanical Properties of δ-TRIP Steels

The δ-TRIP steel has attracted a lot of attention for its potential application in automotive components, owing to the low density, good combination of strength, and ductility. As the difficulty in yield strength further increasement is caused by large fraction ferrite, the work hardening ability was enhanced by optimizing the manganese (Mn)-content in this study. Three δ-TRIP steels with different manganese (Mn)-content were designed to explore the significant effect of Mn content on the work hardening behavior in order to develop high strength steel suitable for the industrial continuous annealing process. The detailed effect of Mn on microstructure evolution and deformation behavior was studied by scanning electron microscope (SEM), interrupted tensile tests, X-ray diffraction (XRD), and in-situ electron backscattered diffraction (EBSD). The study suggested that 2 Mn steel has the lowest degree of bainitic transformation, as a result of fine grain size of prior austenite. The large TRIP effect and dislocation strengthening improve the work hardening rate, resulting in 2 Mn steel exhibiting comparable mechanical properties with the QP980 steels. The retained austenite in 1.5 Mn steel progressively transformed into martensite and sustained the strain to a high strain value of 0.40, showing a good strength-ductility balance.

A Bayesian bias updating procedure for automatic adaptation of soft sensors

Straightforward bias updating procedures for online adjustment of soft sensors (SS) are of interest for industrial processes in which quality or production variables cannot be measured online. This work proposes a Bayesian strategy for automatically updating the bias of a SS from: (i) online measurements of typical process variables and (ii) sporadic laboratory measurements of the critical variable to be estimated. The method continuously monitors the mean and standard deviation of the prediction error (i.e., the difference between the laboratory value and the SS output), and self-adapt the bias without requiring the adjustment of additional parameters. The proposal was evaluated on the basis of simulated examples of an industrial continuous process for the production of Styrene-Butadiene Rubber. The estimates are similar to those obtained with classical methods after optimizing their parameters. Moreover, the bias obtained with the Bayesian approach is preferable in the industrial practice due to its lower variability.