Open AccessArticle
Effects of Inoculum Type and Aeration Flowrate on the Performance of Aerobic Granular SBRs
Processes 2017, 5(3), 41; doi:10.3390/pr5030041 -
Abstract
Aerobic granular sequencing batch reactors (SBRs) are usually inoculated with activated sludge which implies sometimes long start-up periods and high solids concentrations in the effluent due to the initial wash-out of the inoculum. In this work, the use of aerobic mature granules as
[...] Read more.
Aerobic granular sequencing batch reactors (SBRs) are usually inoculated with activated sludge which implies sometimes long start-up periods and high solids concentrations in the effluent due to the initial wash-out of the inoculum. In this work, the use of aerobic mature granules as inoculum in order to improve the start-up period was tested, but no clear differences were observed compared to a reactor inoculated with activated sludge. The effect of the aeration rate on both physical properties of granules and reactor performance was also studied in a stable aerobic granular SBR. The increase of the aeration flow rate caused the decrease of the average diameter of the granules. This fact enhanced the COD and ammonia consumption rates due to the increase of the DO level and the aerobic fraction of the biomass. However, it provoked a loss of the nitrogen removal efficiency due to the worsening of the denitrification capacity as a consequence of a higher aerobic fraction. Full article
Figures

Figure 1

Open AccessArticle
Development of Molecular Distillation Based Simulation and Optimization of Refined Palm Oil Process Based on Response Surface Methodology
Processes 2017, 5(3), 40; doi:10.3390/pr5030040 -
Abstract
The deodorization of the refined palm oil process is simulated here using ASPEN HYSYS. In the absence of a library molecular distillation (MD) process in ASPEN HYSYS, first, a single flash vessel is considered to represent a falling film MD process which is
[...] Read more.
The deodorization of the refined palm oil process is simulated here using ASPEN HYSYS. In the absence of a library molecular distillation (MD) process in ASPEN HYSYS, first, a single flash vessel is considered to represent a falling film MD process which is simulated for a binary system taken from the literature and the model predictions are compared with the published work based on ASPEN PLUS and DISMOL. Second, the developed MD process is extended to simulate the deodorization process. Parameter estimation technique is used to estimate the Antoine’s parameters based on literature data to calculate the pure component vapor pressure. The model predictions are then validated against the patented results of refining edible oil rich in natural carotenes and vitamin E and simulation results were found to be in good agreement, within a ±2% error of the patented results. Third, Response Surface Methodology (RSM) is employed to develop non-linear second-order polynomial equations based model for the deodorization process and the effects of various operating parameters on the performance of the process are studied. Finally, an optimization framework is developed to maximize the concentration of beta-carotene, tocopherol and free fatty acid while optimizing the feed flow rate, temperature and pressure subject to process constrains. The optimum results of feed flow rate, temperature, and pressure were determined as 1291 kg/h, 147 °C and 0.0007 kPa respectively, and the concentration responses of beta- carotene, tocopherol and free fatty acid were found to be 0.000575, 0.000937 and 0.999840 respectively. Full article
Figures

Figure 1

Open AccessFeature PaperArticle
Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing
Processes 2017, 5(3), 39; doi:10.3390/pr5030039 -
Abstract
Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries,
[...] Read more.
Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain. Full article
Figures

Figure 1

Open AccessFeature PaperArticle
Principal Component Analysis of Process Datasets with Missing Values
Processes 2017, 5(3), 38; doi:10.3390/pr5030038 -
Abstract
Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building.
[...] Read more.
Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA), which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets. Full article
Figures

Figure 1

Open AccessFeature PaperArticle
Reduction of Dust Emission by Monodisperse System Technology for Ammonium Nitrate Manufacturing
Processes 2017, 5(3), 37; doi:10.3390/pr5030037 -
Abstract
Prilling is a common process in the fertilizer industry, where the fertilizer melt is converted to droplets that fall, cool down and solidify in a countercurrent flow of air in a prilling tower. A vibratory granulator was used to investigate liquid jet breakup
[...] Read more.
Prilling is a common process in the fertilizer industry, where the fertilizer melt is converted to droplets that fall, cool down and solidify in a countercurrent flow of air in a prilling tower. A vibratory granulator was used to investigate liquid jet breakup into droplets. The breakup of liquid jets subjected to a forced perturbation was investigated in the Rayleigh regime, where a mechanical vibration was applied in order to achieve the production of monodispersed particles. Images of the jet trajectory, breakup, and the formed drops were captured using a high-speed camera. A mathematical model for the liquid outflow conditions based on a transient two-dimensional Navier–Stokes equation was developed and solved analytically, and the correlations between the process parameters of the vibrator and the jet pressure that characterize their disintegration mode were identified. The theoretical predications obtained from the correlations showed a good agreement with the experimental results. Results of the experiments were used to specify the values of the process parameters of the vibration system, and to test them in the production environment in a mode of monodispersed jet disintegration. The vibration frequency was found to have a profound effect on the production of monodispersed particles. The results of experiments in a commercially-sized plant showed that the granulator design based on this study provided prills with a narrower size range compared to the conventional granulators, which resulted in a substantial reduction in dust emission. Full article
Figures

Figure 1

Open AccessFeature PaperOpinion
On the Use of Multivariate Methods for Analysis of Data from Biological Networks
Processes 2017, 5(3), 36; doi:10.3390/pr5030036 -
Abstract
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper
[...] Read more.
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate analysis. Full article
Figures

Figure 1

Open AccessArticle
Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis
Processes 2017, 5(3), 35; doi:10.3390/pr5030035 -
Abstract
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven
[...] Read more.
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos. Full article
Figures

Figure 1

Open AccessFeature PaperReview
Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms
Processes 2017, 5(3), 34; doi:10.3390/pr5030034 -
Abstract
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global
[...] Read more.
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed. Full article
Figures

Figure 1

Open AccessArticle
Techno-Economic Assessment of Benzene Production from Shale Gas
Processes 2017, 5(3), 33; doi:10.3390/pr5030033 -
Abstract
The availability and low cost of shale gas has boosted its use as fuel and as a raw material to produce value-added compounds. Benzene is one of the chemicals that can be obtained from methane, and represents one of the most important compounds
[...] Read more.
The availability and low cost of shale gas has boosted its use as fuel and as a raw material to produce value-added compounds. Benzene is one of the chemicals that can be obtained from methane, and represents one of the most important compounds in the petrochemical industry. It can be synthesized via direct methane aromatization (DMA) or via indirect aromatization (using oxidative coupling of methane). DMA is a direct-conversion process, while indirect aromatization involves several stages. In this work, an economic, energy-saving, and environmental assessment for the production of benzene from shale gas using DMA as a reaction path is presented. A sensitivity analysis was conducted to observe the effect of the operating conditions on the profitability of the process. The results show that production of benzene using shale gas as feedstock can be accomplished with a high return on investment. Full article
Figures

Figure 1

Open AccessArticle
Stoichiometric Network Analysis of Cyanobacterial Acclimation to Photosynthesis-Associated Stresses Identifies Heterotrophic Niches
Processes 2017, 5(2), 32; doi:10.3390/pr5020032 -
Abstract
Metabolic acclimation to photosynthesis-associated stresses was examined in the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 using integrated computational and photobioreactor analyses. A genome-enabled metabolic model, complete with measured biomass composition, was analyzed using ecological resource allocation theory to predict and interpret metabolic acclimation to
[...] Read more.
Metabolic acclimation to photosynthesis-associated stresses was examined in the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 using integrated computational and photobioreactor analyses. A genome-enabled metabolic model, complete with measured biomass composition, was analyzed using ecological resource allocation theory to predict and interpret metabolic acclimation to irradiance, O2, and nutrient stresses. Reduced growth efficiency, shifts in photosystem utilization, changes in photorespiration strategies, and differing byproduct secretion patterns were predicted to occur along culturing stress gradients. These predictions were compared with photobioreactor physiological data and previously published transcriptomic data and found to be highly consistent with observations, providing a systems-based rationale for the culture phenotypes. The analysis also indicated that cyanobacterial stress acclimation strategies created niches for heterotrophic organisms and that heterotrophic activity could enhance cyanobacterial stress tolerance by removing inhibitory metabolic byproducts. This study provides mechanistic insight into stress acclimation strategies in photoautotrophs and establishes a framework for predicting, designing, and engineering both axenic and photoautotrophic-heterotrophic systems as a function of controllable parameters. Full article
Figures

Open AccessEditorial
Special Issue: Water Soluble Polymers
Processes 2017, 5(2), 31; doi:10.3390/pr5020031 -
Open AccessArticle
Closed-Loop Characterization of Neuronal Activation Using Electrical Stimulation and Optical Imaging
Processes 2017, 5(2), 30; doi:10.3390/pr5020030 -
Abstract
We have developed a closed-loop, high-throughput system that applies electrical stimulation and optical recording to facilitate the rapid characterization of extracellular, stimulus-evoked neuronal activity. In our system, a microelectrode array delivers current pulses to a dissociated neuronal culture treated with a calcium-sensitive fluorescent
[...] Read more.
We have developed a closed-loop, high-throughput system that applies electrical stimulation and optical recording to facilitate the rapid characterization of extracellular, stimulus-evoked neuronal activity. In our system, a microelectrode array delivers current pulses to a dissociated neuronal culture treated with a calcium-sensitive fluorescent dye; automated real-time image processing of high-speed digital video identifies the neuronal response; and an optimized search routine alters the applied stimulus to achieve a targeted response. Action potentials are detected by measuring the post-stimulus, calcium-sensitive fluorescence at the neuronal somata. The system controller performs directed searches within the strength–duration (SD) stimulus-parameter space to build probabilistic neuronal activation curves. This closed-loop system reduces the number of stimuli needed to estimate the activation curves when compared to the more commonly used open-loop approach. This reduction allows the closed-loop system to probe the stimulus regions of interest in the multi-parameter waveform space with increased resolution. A sigmoid model was fit to the stimulus-evoked activation data in both current (strength) and pulse width (duration) parameter slices through the waveform space. The two-dimensional analysis results in a set of probability isoclines corresponding to each neuron–electrode pair. An SD threshold model was then fit to the isocline data. We demonstrate that a closed-loop methodology applied to our imaging and micro-stimulation system enables the study of neuronal excitation across a large parameter space. Full article
Figures

Figure 1

Open AccessArticle
Structural Properties of Dynamic Systems Biology Models: Identifiability, Reachability, and Initial Conditions
Processes 2017, 5(2), 29; doi:10.3390/pr5020029 -
Abstract
Abstract: Dynamic modelling is a powerful tool for studying biological networks. Reachability (controllability), observability, and structural identifiability are classical system-theoretic properties of dynamical models. A model is structurally identifiable if the values of its parameters can in principle be determined from observations of
[...] Read more.
Abstract: Dynamic modelling is a powerful tool for studying biological networks. Reachability (controllability), observability, and structural identifiability are classical system-theoretic properties of dynamical models. A model is structurally identifiable if the values of its parameters can in principle be determined from observations of its outputs. If model parameters are considered as constant state variables, structural identifiability can be studied as a generalization of observability. Thus, it is possible to assess the identifiability of a nonlinear model by checking the rank of its augmented observability matrix. When such rank test is performed symbolically, the result is of general validity for almost all numerical values of the variables. However, for special cases, such as specific values of the initial conditions, the result of such test can be misleading—that is, a structurally unidentifiable model may be classified as identifiable. An augmented observability rank test that specializes the symbolic states to particular numerical values can give hints of the existence of this problem. Sometimes it is possible to find such problematic values analytically, or via optimization. This manuscript proposes procedures for performing these tasks and discusses the relation between loss of identifiability and loss of reachability, using several case studies of biochemical networks. Full article
Figures

Figure 1

Open AccessFeature PaperArticle
Outlier Detection in Dynamic Systems with Multiple Operating Points and Application to Improve Industrial Flare Monitoring
Processes 2017, 5(2), 28; doi:10.3390/pr5020028 -
Abstract
In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in
[...] Read more.
In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time series Kalman filter (TSKF) with the pruned exact linear time (PELT) approach to detect outliers is proposed. The proposed method outperformed benchmark methods in outlier removal performance using simulated data sets of dynamic systems with mean shifts. The method was also able to maintain the integrity of the original data set after performing outlier removal. In addition, the methodology was tested on industrial flaring data to pre-process the flare data for discriminant analysis. The industrial test case shows that performing outlier removal dramatically improves flare monitoring results through Partial Least Squares Discriminant Analysis (PLS-DA), which further confirms the importance of data cleaning in process data analytics. Full article
Figures

Figure 1

Open AccessEditorial
Special Issue “Real-Time Optimization” of Processes
Processes 2017, 5(2), 27; doi:10.3390/pr5020027 -
Abstract Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints.[...] Full article
Open AccessFeature PaperArticle
Comparison of Polymer Networks Synthesized by Conventional Free Radical and RAFT Copolymerization Processes in Supercritical Carbon Dioxide
Processes 2017, 5(2), 26; doi:10.3390/pr5020026 -
Abstract
There is a debate in the literature on whether or not polymer networks synthesized by reversible deactivation radical polymerization (RDRP) processes, such as reversible addition-fragmentation radical transfer (RAFT) copolymerization of vinyl/divinyl monomers, are less heterogeneous than those synthesized by conventional free radical copolymerization
[...] Read more.
There is a debate in the literature on whether or not polymer networks synthesized by reversible deactivation radical polymerization (RDRP) processes, such as reversible addition-fragmentation radical transfer (RAFT) copolymerization of vinyl/divinyl monomers, are less heterogeneous than those synthesized by conventional free radical copolymerization (FRP). In this contribution, the syntheses by FRP and RAFT of hydrogels based on 2-hydroxyethylene methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) in supercritical carbon dioxide (scCO2), using Krytox 157 FSL as the dispersing agent, and the properties of the materials produced, are compared. The materials were characterized by differential scanning calorimetry (DSC), swelling index (SI), infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Studies on ciprofloxacin loading and release rate from hydrogels were also carried out. The combined results show that the hydrogels synthesized by FRP and RAFT are significantly different, with apparently less heterogeneity present in the materials synthesized by RAFT copolymerization. A ratio of experimental (Mcexp) to theoretical (Mctheo) molecular weight between crosslinks was established as a quantitative tool to assess the degree of heterogeneity of a polymer network. Full article
Figures

Figure 1

Open AccessArticle
Design of Cross-Linked Starch Nanocapsules for Enzyme-Triggered Release of Hydrophilic Compounds
Processes 2017, 5(2), 25; doi:10.3390/pr5020025 -
Abstract
Cross-linked starch nanocapsules (NCs) were synthesized by interfacial polymerization carried out using the inverse mini-emulsion technique. 2,4-toluene diisocyanate (TDI) was used as the cross-linker. The influence of TDI concentrations on the polymeric shell, particle size, and encapsulation efficiency of a hydrophilic dye, sulforhodamine
[...] Read more.
Cross-linked starch nanocapsules (NCs) were synthesized by interfacial polymerization carried out using the inverse mini-emulsion technique. 2,4-toluene diisocyanate (TDI) was used as the cross-linker. The influence of TDI concentrations on the polymeric shell, particle size, and encapsulation efficiency of a hydrophilic dye, sulforhodamine 101 (SR 101), was investigated by Fourier transform infrared (FT-IR) spectroscopy, dynamic light scattering (DLS), and fluorescence measurements, respectively. The final NC morphology was confirmed by scanning electron microscopy. The leakage of SR 101 through the shell of NCs was monitored at 37 °C for seven days, and afterwards the NCs were redispersed in water. Depending on cross-linker content, permeable and impermeable NCs shell could be designed. Enzyme-triggered release of SR 101 through impermeable NC shells was investigated using UV spectroscopy with different α-amylase concentrations. Impermeable NCs shell were able to release their cargo upon addition of amylase, being suitable for a drug delivery system of hydrophilic compounds. Full article
Figures

Figure 1

Open AccessReview
Applications of Water-Soluble Polymers in Turbulent Drag Reduction
Processes 2017, 5(2), 24; doi:10.3390/pr5020024 -
Abstract
Water-soluble polymers with high molecular weights are known to decrease the frictional drag in turbulent flow very effectively at concentrations of tens or hundreds of ppm. This drag reduction efficiency of water-soluble polymers is well known to be closely associated with the flow
[...] Read more.
Water-soluble polymers with high molecular weights are known to decrease the frictional drag in turbulent flow very effectively at concentrations of tens or hundreds of ppm. This drag reduction efficiency of water-soluble polymers is well known to be closely associated with the flow conditions and rheological, physical, and/or chemical characteristics of the polymers added. Among the many promising polymers introduced in the past several decades, this review focuses on recent progress in the drag reduction capability of various water-soluble macromolecules in turbulent flow including both synthetic and natural polymers such as poly(ethylene oxide), poly(acrylic acid), polyacrylamide, poly(N-vinyl formamide), gums, and DNA. The polymeric species, experimental parameters, and numerical analysis of these water-soluble polymers in turbulent drag reduction are highlighted, along with several existing and potential applications. The proposed drag reduction mechanisms are also discussed based on recent experimental and numerical researches. This article will be helpful to the readers to understand better the complex behaviors of a turbulent flow with various water-soluble polymeric additives regarding experimental conditions, drag reduction mechanisms, and related applications. Full article
Figures

Figure 1

Open AccessFeature PaperArticle
Aqueous Free-Radical Polymerization of Non-Ionized and Fully Ionized Methacrylic Acid
Processes 2017, 5(2), 23; doi:10.3390/pr5020023 -
Abstract
Water-soluble, carboxylic acid monomers are known to exhibit peculiar kinetics when polymerized in aqueous solution. Namely, their free-radical polymerization rate is affected by several parameters such as monomer concentration, ionic strength, and pH. Focusing on methacrylic acid (MAA), even though this monomer has
[...] Read more.
Water-soluble, carboxylic acid monomers are known to exhibit peculiar kinetics when polymerized in aqueous solution. Namely, their free-radical polymerization rate is affected by several parameters such as monomer concentration, ionic strength, and pH. Focusing on methacrylic acid (MAA), even though this monomer has been largely addressed, a systematic investigation of the effects of the above-mentioned parameters on its polymerization rate is missing, in particular in the fully ionized case. In this work, the kinetics of non-ionized and fully ionized MAA are characterized by in-situ nuclear magnetic resonance (NMR). Such accurate monitoring of the reaction rate enables the identification of relevant but substantially different effects of the monomer and electrolyte concentration on polymerization rate in the two ionization cases. For non-ionized MAA, the development of a kinetic model based on literature rate coefficients allows us to nicely simulate the experimental data of conversion versus time at a high monomer concentration. For fully ionized MAA, a novel propagation rate law accounting for the electrostatic interactions is proposed: the corresponding model is capable of predicting reasonably well the electrolyte concentration effect on polymerization rate. Nevertheless, further kinetic information in a wider range of monomer concentrations would be welcome to increase the reliability of the model predictions. Full article
Figures

Open AccessArticle
Analyzing the Mixing Dynamics of an Industrial Batch Bin Blender via Discrete Element Modeling Method
Processes 2017, 5(2), 22; doi:10.3390/pr5020022 -
Abstract
A discrete element model (DEM) has been developed for an industrial batch bin blender in which three different types of materials are mixed. The mixing dynamics have been evaluated from a model-based study with respect to the blend critical quality attributes (CQAs) which
[...] Read more.
A discrete element model (DEM) has been developed for an industrial batch bin blender in which three different types of materials are mixed. The mixing dynamics have been evaluated from a model-based study with respect to the blend critical quality attributes (CQAs) which are relative standard deviation (RSD) and segregation intensity. In the actual industrial setup, a sensor mounted on the blender lid is used to determine the blend composition in this region. A model-based analysis has been used to understand the mixing efficiency in the other zones inside the blender and to determine if the data obtained near the blender-lid region are able to provide a good representation of the overall blend quality. Sub-optimal mixing zones have been identified and other potential sampling locations have been investigated in order to obtain a good approximation of the blend variability. The model has been used to study how the mixing efficiency can be improved by varying the key processing parameters, i.e., blender RPM/speed, fill level/volume and loading order. Both segregation intensity and RSD reduce at a lower fill level and higher blender RPM and are a function of the mixing time. This work demonstrates the use of a model-based approach to improve process knowledge regarding a pharmaceutical mixing process. The model can be used to acquire qualitative information about the influence of different critical process parameters and equipment geometry on the mixing dynamics. Full article
Figures