DOE Model Research Articles

Comparing a polynomial DOE model and an ANN model for enhanced geranyl cinnamate biosynthesis with Novozym® 435 lipase

Central Composite Rotatable Design (CCRD), a type of factorial design of experiment is among the most traditional methods used for optimizing bioprocesses, but, in recent years artificial neural networks (ANNs) have emerged as a promising approach for data modeling in bioprocesses. A comparative study between CCRD and ANN modeling for data treatment in the optimization of geranyl cinnamate biosynthesis using Novozym® 435 lipase was conducted. The most effective ANN architecture identified for predicting and maximizing the enzymatic synthesis of geranyl cinnamate was a 3-3-1 neurons model utilizing logsig activation function in the hidden layer. The ANN was trained using all available experimental data and demonstrated a strong fit to the experimental data, coefficient of determination near 1 (R2 = 0.9948) and low Sum-squared Error (SSE = 43.06). The polynomial model (CCRD; R2 = 0.9806; SSE = 161.56) indicated the same optimal conditions as the ANN model, predicting a temperature of 90.2 °C, molar ratio of 1:5.68, and enzyme concentration of 18.6% w/w. Comparison of R2 and SSE values due to lack of fit between both models suggests that ANN predictions closely align with experimental conversion values of geranyl cinnamate. Although the polynomial model is feasible to be applied in enzymatic synthesis, it may be less precise in its predictions than ANN models.

Process Optimization of the Morphological Properties of Epoxy Resin Molding Compounds Using Response Surface Design.

An epoxy compound's polymer structure can be characterized by the glass transition temperature (Tg) which is often seen as the primary morphological characteristic. Determining the Tg after manufacturing thermoset-molded parts is an important objective in material characterization. To characterize quantitatively the dependence of Tg on the degree of cure, the DiBenedetto equation is usually used. Monitoring polymer network formation during molding processes is therefore one of the most challenging tasks in polymer processing and can be achieved using dielectric analysis (DEA). In this study, the morphological properties of an epoxy resin-based molding compounds (EMC) were optimized for the molding process using response surface analysis. Processing parameters such as curing temperature, curing time, and injection rate were investigated according to a DoE strategy and analyzed as the main factors affecting Tg as well as the degree of cure. A new method to measure the Tg at a certain degree of cure was developed based on warpage analysis. The degree of cure was determined inline via dielectric analysis (DEA) and offline using differential scanning calorimetry (DSC). The results were used as the response in the DoE models. The use of the DiBenedetto equation to refine the response characteristics for a wide range of process parameters has significantly improved the quality of response surface models based on the DoE approach.

Investigation of DOE model analyses for open atmosphere laser polishing of additively manufactured Ti-6Al-4V samples by using ANOVA

Investigation of DOE model analyses for open atmosphere laser polishing of additively manufactured Ti-6Al-4V samples by using ANOVA

Dive into diagnostics for DOE model discrepancies

Dive into diagnostics for DOE model discrepancies

Prediction and performance optimisation of a DI CI engine fuelled diesel–Bael biodiesel blends with DMC additive using RSM and ANN: Energy and exergy analysis

Synthesis of biofuel from bioresources represents one of the greatest propitious options for achieving a cleaner production of energy and the global circular bio-economy. The superior cetane rating of biodiesel makes it a fitting fuel for CI diesel engines. The present study aims to valorise Bael seeds for biodiesel production. Bael is a species of treeindigenoustoIndia. The diesel–Bael biodiesel was blended with dimethyl carbonate (DMC) as an ignition enhancer and investigated in a selected diesel engine. This work reports that the Bael biodiesel blend gets a tolerable level of BTE against neat fossil diesel. An utmost BTE of 30.68% was achieved with B15DMC5 which is very close to diesel (31.8%) at peak load. Certainly, BSFC was suppressed and emissions lessened by DMC inclusion. For instance, the CO and HC emissions substantially reduced to a minimum of 0.19% and 179 ppm with the B17.5DMC2.5 blend at peak load. NOx emission is directly proportional to Bael biodiesel concentration, Notably, NOx emission is well controlled at 50% and 75% load even though with the increasing biodiesel concentration in the blend. Though, control of emissions at peak load is still a crucial issue. Nevertheless, the DMC additives controlled the NOx emission at peak load. B15DMC5 blend exhibits progress in combustion through the optimized value of HRR, CGP, and CGT against B20. The presence of NOx and smoke opacity without additives is suppressed up to 7% and 10% with additives. Availability (exergy) and exergy efficiency at different loads of both neat diesel and blended fuels, mainly shaft, cooling water, and exhaust availability were calculated. The results of the study illustrated that the input availability increased at peak loads, as well as gross work output, was maximum at peak load due to higher fuel exergy present in the combustion chamber. A maximum exergy efficiency of 68% was recorded by B15DMC5 at peak load. Finally, the process optimization by RSM has been validated by experimental results and further authenticated with ANN. The results of the RSM model promote it as statistically significant. Generate the regression equation for all output responses. The optimized operating parameters of engine load of 50%, 10% of biodiesel blend, and additive concentration of 3.59% achieved the most optimum output response. The results of the predicted optimized condition of BTE, BSFC, CO, HC, NOx, and Smoke opacity showed 24.051%, 0.441861 kg/kW hr, 0.2028%, 113.607 ppm, 316.099 ppm, and 14.4286% respectively. The R2 values from RSM and ANN DoE models were 0.992834 and 0.99984. This study concludes that a superior precision certainty from both RSM and ANN models is beneficial for engine overall performance estimation. This phenomenon provides a good sign to implement the ANN to forecast engine operational behavior in the future. The novel approach of process optimization by RSM and ANN for Bael biodiesel with DMC blended fuel operate DI CI engine supports the SDGs of the UN such as Good health and Well-being (SDG: 3), Affordable and Clean Energy (SDG: 7), Responsible Consumption and Production (SDG: 12), Climate Action (SDG: 13) and Life on Land (15).

Pristup baziran na Dizajnu eksperimenata za poboljšanje procesa suvog mešanja u proizvodnji tableta sa niskom dozom Alprazolama uz primenu Raman spektroskopije za praćenje uniformnosti sadržaja

A low-dose tablet formulation, containing a potent Benzodiazepine derivative Alprazolam was developed, considering the achievement of appropriate content uniformity of the active substance in powder blends and tablets as a major challenge. Two different types of lactose monohydrate (Tablettose 80 and Granulac 200) and two different types of dry mixing processes (high-shear mixing and "in bulk" mixing) were employed. To evaluate the influence of the variables (mixing speed, mixing time, filling level of the high-shear and cube mixer, lactose monohydrate type) and their interactions upon the response (content uniformity of Alprazolam in the powder blends), a Factorial 2 4 design (with 4 factors at 2 levels in 1 block) was generated for each type of mixer. For high-shear dry mixing the Response Surface, D-optimal Factorial 2 4 design (with 2 replications and 31 experiments) was used, while for the "in bulk" dry mixing the Response Surface, Central Composite Factorial 2 4 design (with 34 experiments) was used. The process parameters for the high-shear mixer were varied within the following ranges: filling level of 70-100%, impeller mixing speed of 50-300 rpm and mixing time of 2-10 minutes. For the cube mixer the following process parameter ranges were employed: filling level of 30-60%, mixing speed of 20-390 rpm and mixing time of 2-10 minutes. Raman spectroscopy in conjunction with a validated Partial Least Square (PLS) regression model was used as a Process Analytical Technology (PAT) tool for Alprazolam content determination and content uniformity monitoring. The DoE model was further employed to optimize the powder blending process in regard to the achievement of appropriate Alprazolam content uniformity using high-shear mixing and Tabletosse 80 as filler. The desirability function revealed that the following process parameters: a mixing time of 2 minutes, a mixing speed of 300 rpm and a 70% filling level of the mixer would produce powder blends with the lowest variability in Alprazolam content. The three independent lab batches of low-dose Alprazolam tablets, produced with high-shear mixing using these process parameters, conformed to the requirements of the European Pharmacopoeia for content (assay) of Alprazolam and uniformity of the dosage units.

Neutronic comparative analysis between DOE and CEA model designs for fourth generation 2400 MW-th gas-cooled fast reactor

Neutronic comparative analysis between DOE and CEA model designs for fourth generation 2400 MW-th gas-cooled fast reactor

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

Large-Scale Cyclopropanation of Butyl Acrylate with Difluorocarbene and Classical Resolution of a Key Fluorinated Building Block

To address challenges in the preparation of a key building block containing a difluorocyclopropane moiety, we have developed a new protocol for difluorocarbene generation that relies on a Krapcho-type dealkylation of ethyl bromodifluoroacetate (EBDFA), an inexpensive and readily available fluorinated feedstock. Application of DoE and kinetic modeling was used to understand key reaction parameters and identify an optimal process. We report two variants of this procedure that offer different processing advantages and that have both been scaled successfully multiple times to deliver hundreds of kilograms of the resulting difluorocyclopropane. To access a single enantiomer of the target compound, we have also developed a classical resolution strategy and recycling protocol for the undesired enantiomer to replace previous chromatographic methods for separation.

Phase I process monitoring: The case of the balanced one‐way random effects model

AbstractPhase I is crucial for the success of the overall statistical process control (SPC) and monitoring regime. Shewhart‐type charts are recommended in this phase because of their broader shift detection ability. In this paper, a Phase I Shewhart‐type chart is considered for the balanced random effects (also called a variance components) model. The proposed methodology takes proper account of the effects of parameter estimation and uses the false alarm probability (FAP) metric to design the chart. In the sequel, the corrected (adjusted) charting constants are calculated and tabulated. The constant can be found, on demand, from an accompanying R package. Motivations and illustrations with some real data are provided. Performance of the chart is examined in terms of in‐control robustness and detection of nonhomogeneity (out‐of‐control). The proposed chart is shown to be easily adaptable to more general models, with more variance components and nested factors, and can accommodate various estimators of variance. Thus, it enables a broader Phase I process monitoring strategy, under normality, which can be applied within the ANOVA framework applicable for many DOE models. A summary and some recommendations are provided.

A Novel Approach to Optimize Hot Melt Impregnation in Terms of Amorphization Efficiency.

In this study, an innovative methodology to optimize amorphization during the hot melt impregnation (HMI) process was proposed. The novelty of this report revolves around the use of thermal analysis in combination with design of experiments (DoEs) to reduce residual crystallinity during the HMI process. As a model formulation, a mixture of ibuprofen (IBU) and Neusilin was used. The main aim of the study was to identify the critical process parameters of HMI and determine their optimal values to assure a robust impregnation process and possibly the highest possible amorphization rate of IBU. In order to realize this, a DoE approach was proposed based on a face-centered composite design involving three factors. The IBU/Neusilin ratio, the feeding rate, and the screw speed were considered as variables, while the residual crystallinity level of IBU, determined using differential scanning calorimetry (DSC), was measured as the response. Additionally, the stability of IBU under HMI was analyzed using high-performance liquid chromatography to estimate the extent of potential degradation. In order to verify the correctness of the DoE model, tested extrudates were manufactured by HMI and the obtained extrudates were thoroughly examined using scanning electron micrography, X-ray powder diffraction, and DSC.

Uniting laboratory automation, DoE data, and modeling techniques to accelerate chemical process development

Rapid knowledge building of chemical processes with highly automated DoE (HAD) and statistical analyses and modeling.

Modelowanie sił w szlifowaniu obwodowym płaszczyzn według różnych planów badawczych

The paper presents forces modeling with the use of DoE models, such as (Box-Wilson) central composite design in face centered variant (CCF) and Box-Behnken design in a surface peripheral grinding process of 100Cr6 steel with M3X60K5VE01-35 grinding wheel. Experiment design and result analysis were done with the use of Design-Expert software. Force models, obtained with application of selected designs of experiment, were compared on the basis of the coefficient of determination, and values of residual standard deviation.

RETRACTED ARTICLE: Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification

This paper elucidates a data predicting model using an intelligent rule-based enhanced multiclass support vector machine and fuzzy rules (IREMSVM-FR) while optimizing the test practices and trials needed for the proportioning of self-compacting concrete (SCC) using response surface methodology (RSM). The SCC requires a wide range of material content, and hence, more numbers of investigations were typically essential to select a suitable mixture to get the required properties of SCC. Taguchi’s methodology with an L18 array and three-level factor was used to reduce the number of the experiment. Four regulating elements, i.e., cement, fly ash, water powder ratio and superplasticizer, were used. Two results such as slump flow in the fresh state and the compressive strength in the hardened state at 28 days were assessed. Optimizations of the results were set by using RSM. The reactions of material parameters examined to optimize the fresh and hardened properties such as slump flow and compressive strength of SCC. The full quadratic equation of a model can be used to assess the influence of constituent materials on the properties of SCC. Moreover, these 28-days observation records are considered as SCC dataset. For predicting the properties of SCC, an existing intelligent classification algorithm IREMSVM-FR has been used. In which cement (kg), fly ash (kg), water powder ratio (W/P) and superplasticizer (l/m3) were taken as sources of data, whereas slump flow and compressive strength were the responses. It is revealed from the results that RSM has optimized the test procedures and trials needed for the proportioning of SCC so as to maximize the slump flow and compressive strength effectively than DOE and IREMSVM model have conformed.

R software package based statistical optimization of process components to simultaneously enhance the bacterial growth, laccase production and textile dye decolorization with cytotoxicity study.

The thermophilic bacterium, Bacillus licheniformis U1 is used for the optimization of bacterial growth (R1), laccase production (R2) and synthetic disperse blue DBR textile dye decolorization (R3) in the present study. Preliminary optimization has been performed by one variable at time (OVAT) approach using four media components viz., dye concentration, copper sulphate concentration, pH, and inoculum size. Based on OVAT result further statistical optimization of R1, R2 and R3 performed by Box–Behnken design (BBD) using response surface methodology (RSM) in R software with R Commander package. The total 29 experimental runs conducted in the experimental design study towards the construction of a quadratic model. The model indicated that dye concentration 110 ppm, copper sulphate 0.2 mM, pH 7.5 and inoculum size 6% v/v were found to be optimum to maximize the laccase production and bacterial growth. Whereas, maximum dye decolorization achieved in media containing dye concentration 110 ppm, copper sulphate 0.6 mM, pH 6 and inoculum size 6% v/v. R package predicted R2 of R1, R2 and R3 were 0.9917, 0.9831 and 0.9703 respectively; likened to Design-Expert (Stat-Ease) (DOE) predicted R2 of R1, R2, and R3 were 0.9893, 0.9822 and 0.8442 respectively. The values obtained by R software were more precise, reliable and reproducible, compared to the DOE model. The laccase production was 1.80 fold increased, and 2.24 fold enhancement in dye decolorization was achieved using optimized medium than initial experiments. Moreover, the laccase-treated sample demonstrated the less cytotoxic effect on L132 and MCF-7 cell lines compared to untreated sample using MTT assay. Higher cell viability and lower cytotoxicity observed in a laccase-treated sample suggest the impending application of bacterial laccase in the reduction of toxicity of dye to design rapid biodegradation process.

Thermodynamic, Environmental, and Economic Analysis of Electrosynthesis of Hydrogen Fuel with State-of-the-Art Solid Oxide Electrolyzers

Gaia Energy Research Institute (Gaia) presents results for (1) a life cycle analysis (LCA) and (2) a technoeconomic analysis (TEA) of a state-of-the-art solid oxide electrolyzer (SOE) for electrosynthesis of hydrogen fuel. The significance of this work is that it quantifies potential reductions in greenhouse gas emissions (GHGs) and other environmental impacts with the use of SOEs. The significance of this work is that it also quantifies the potential future cost of producing extremely pure hydrogen using SOEs in dollars per kilogram of hydrogen ($/kg H2). 1: Life cycle analysis LCA results indicate that GHGs from the SOE can be lower than GHGs from steam methane reforming (SMR) of natural gas (the most prevalent technology for making hydrogen). To obtain this result, Gaia builds on the existing DOE models, including, but not limited to, the Argonne National Laboratory full life-cycle model, GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation.) SOE GHG emissions are also evaluated through single-variable sensitivity studies and Tornado charts. Model results indicate that some of the input variables that are most impactful to GHGs include, but are not limited to, (1) the SOE stack and system electricity usages (kWh_electric/kg H2), (2) the SOE stack and system heat usages (kWh_thermal /kg H2), (3) the carbon footprint of the electricity input source to the SOE (kg carbon /kWh_electric), and (4) the carbon footprint of the thermal input source to the SOE (kg carbon /kWh_thermal). Also quantified are potential future changes in GHG emissions that result from further SOE technology advancements. These results are plotted in Waterfall Charts. In addition to GHGs, this analysis also considers life cycle air pollution emissions and solid waste streams. 2: Technoeconomic analysis TEA results indicate that the SOE is estimated to produce hydrogen at less than $2/kg H2, which is the DOE Fuel Cell Technologies Office’s (FCTO) H2 production cost target. To obtain this result, Gaia builds on existing DOE FCTO H2 production analysis modelling tools, including the H2A tools, and the existing FCTO SOE case studies and Excel-based models. Gaia deploys the FCTO's Hydrogen Analysis tools, including the H2A Production and H2A Delivery Scenario Analysis Model (HDSAM) models, to estimate the levelized cost of (1) producing, (2) delivering, (3) compressing, (4) storing and (5) dispensing H2 ($/kg H2). The levelized cost of producing H2 from SOEs is also analyzed using single-variable sensitivity studies and Tornado charts. (An important caveat is that H2A model results do not indicate optimal results for engineering design parameters or market conditions.) Model results indicate that some of the input variables that are most impactful to the levelized cost of producing H2 from SOEs include, but are not limited to, (1) the electricity price, (2) the purchase price for external heat going into the SOE stack, (3) the SOE stack and system capital costs, (4) the SOE stack and system electricity usages, (5) the SOE stack and system heat usages, (6) SOE stack and system lifetimes, and (7) the operating capacity factor. System costs include both stack and balance of plant costs. Also quantified are potential improvements in the levelized H2 production that result from further SOE technology advancements. These results are plotted in Waterfall Charts. Both current and future cases were analyzed for H2 generation with state-of-the-art SOE electrolyzers using the DOE’s H2A Production Model. For the state-of-the-art SOE system design analyzed here, uninstalled SOE system capital costs are expected to decline from $840/kWe to $640/kWe between current and future cases. Electricity usage is expected to decline from 37.61 kWh_e/kg H2 to 35.1 kWh_e/kg H2 between current and future cases. The current case reflects a ~$3 /kg levelized H2 production cost, based on an average cost of electricity of 2.5¢/kWh. The future case reflects a <$2 /kg levelized H2 production cost, based on an average cost of electricity of 2¢/kWh. Capital costs are the primary cost driver in current and future cases. The secondary cost driver is electricity costs. Between the current and the future case, the estimated levelized H2 production cost declines due to expected decreases in (1) SOE system capital costs (primarily at the stack), (2) indirect capital costs and replacement costs, (3) system energy consumption, (4) electricity price, and (5) fixed operations and maintenance costs. The LCA shows a 79% to 100% reduction in GHGs for hydrogen from the state-of-the-art SOE system analyzed here, compared with hydrogen from natural gas SMR.

Key Effects and Process Parameters Extraction on the CD of Reactive Ion Etching (RIE) Based on DOE Modeling

The critical dimension (CD) is highly influenced by the reactive ion etching (RIE) of silicon in the CMOS technology. The CD has to be well-controlled since it is one of the most important features related to process stability and product quality. However, the RIE process involves a lot of parameters which are highly confounded and entangled and thus it is very difficult to analyze the process. To study and extract the influences of these parameters in terms of CD variation, screening experiments are carried out to obtain the necessary factors to develop a CD-related model. An elaborated model, based on the selected parameters, is then built up via applying the design of experiments and response surface optimization. This model is validated to be capable of characterizing the process as well as predicting the CD.

Life Cycle and Techno-Economic Analysis of State-of-the-Art Solid Oxide Electrolyzer Systems

Gaia Energy Research Institute (Gaia) presents results for (1) a life cycle analysis (LCA) and (2) a technoeconomic analysis (TEA) of a state-of-the-art solid oxide electrolyzer (SOE). Gaia analyzes a state-of-the-art SOE for making hydrogen, based on an SOE design by Ceramatec Inc. The significance of this work is that it quantifies potential reductions in greenhouse gas emissions (GHGs) and other environmental impacts with the use of SOEs. The significance of this work is that it also quantifies the potential future cost of producing extremely pure hydrogen using SOEs in dollars per kilogram of hydrogen ($/kg H2). 1: Life cycle analysis LCA results indicate that GHGs from the SOE can be at least ten percent (10%) lower than GHGs from steam methane reforming (SMR) of natural gas (the most prevalent technology for making hydrogen). To obtain this result, Gaia builds on the existing DOE models, including, but not limited to, the Argonne National Laboratory full life-cycle model, GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation.) SOE GHG emissions are also evaluated through single-variable sensitivity studies and Tornado charts. Model results indicate that some of the input variables that are most impactful to GHGs include, but are not limited to, (1) the SOE stack and system electricity usages (kWh_electric/kg H2), (2) the SOE stack and system heat usages (kWh_thermal /kg H2), (3) the carbon footprint of the electricity input source to the SOE (kg carbon /kWh_electric), and (4) the carbon footprint of the thermal input source to the SOE (kg carbon /kWh_thermal). Also quantified are potential future changes in GHG emissions that result from further SOE technology advancements. These results are plotted in Waterfall Charts. Consideration also is given to scenarios in which H2 is delivered above ambient pressure, and where the energy input to the SOE includes intermittent renewable energy sources. In addition to GHGs, this analysis also considers life cycle air pollution emissions and solid waste streams. 2: Technoeconomic analysis TEA results indicate that the SOE is estimated to produce hydrogen at less than $2/kg H2, which is the DOE Fuel Cell Technologies Office’s (FCTO) H2 production cost target. To obtain this result, Gaia builds on existing DOE FCTO H2 production analysis modelling tools, including the H2A tools, and the existing FCTO SOE case studies and Excel-based models. Gaia deploys the FCTO's Hydrogen Analysis tools, including the H2A Production and H2A Delivery Scenario Analysis Model (HDSAM) models, to estimate the levelized cost of (1) producing, (2) delivering, (3) compressing, (4) storing and (5) dispensing H2 ($/kg H2). The levelized cost of producing H2 from SOEs is also analyzed using single-variable sensitivity studies and Tornado charts. Model results indicate that some of the input variables that are most impactful to the levelized cost of producing H2 from SOEs include, but are not limited to, (1) the electricity price (¢/kWh_electric), (2) the purchase price for external heat going into the SOE stack (¢/kWh_thermal), (3) the SOE stack and system capital costs ($/kW), (4) the SOE stack and system electricity usages (kWh_electric/kg H2), (5) the SOE stack and system heat usages (kWh_thermal/kg H2), (6) SOE stack and system lifetimes (years), and (7) the operating capacity factor (percent). System costs include both stack and balance of plant (BOP) costs. Also quantified are potential improvements in the levelized H2 production that result from further SOE technology advancements. These results are plotted in Waterfall Charts. Consideration also is given to scenarios in which H2 is delivered above ambient pressure, and where the energy input to the SOE includes intermittent renewable energy sources.

Formulation design space for stable, pH sensitive crystalline nifedipine nanoparticles

Enteric coated formulations protect drugs from degrading in the harsh environment of the stomach (acidic pH and enzymes), and promotes drug delivery to and absorption into the duodenum and/or later parts of the intestine. Four DoE models were applied to optimize formulation parameters for the preparation of pH sensitive nifedipine nanoparticles. Stability studies were performed on the optimized formulations to monitor any possible variation in particle size distribution, homogeneity index, surface charge and drug release (pH 1.2 and pH 6.8). Stability studies were performed for 3 months at 4°C, 25°C and 40°C. A combination of Eudragit®L 100-55 and polyvinyl alcohol was determined to be the most effective in stabilizing the nanoparticle suspension. The average particle size distribution, polydispersity index and surface charge of the optimized pH sensitive nifedipine nanoparticles were determined to be 131.86±8.21nm, 0.135±0.008 and −7.631±0.146mV, respectively. Following three months storage, it was observed that the formulations stored at 4°C were stable in terms of particle size distribution, polydispersity index, surface charge, drug loading and drug release, whereas those stored at 25°C and 40°C were relatively unstable. A predictive model to prepare stable pH sensitive nifedipine nanoparticles, was successfully developed using multiple linear regression analysis.

Process design and control of a twin screw hot melt extrusion for continuous pharmaceutical tamper-resistant tablet production

Hot melt extrusion (HME) is a well-known process within the plastic and food industries that has been utilized for the past several decades and is increasingly accepted by the pharmaceutical industry for continuous manufacturing. For tamper-resistant formulations of e.g. opioids, HME is the most efficient production technique. The focus of this study is thus to evaluate the manufacturability of the HME process for tamper-resistant formulations. Parameters such as the specific mechanical energy (SME), as well as the melt pressure and its standard deviation, are important and will be discussed in this study.In the first step, the existing process data are analyzed by means of multivariate data analysis. Key critical process parameters such as feed rate, screw speed, and the concentration of the API in the polymers are identified, and critical quality parameters of the tablet are defined. In the second step, a relationship between the critical material, product and process quality attributes are established by means of Design of Experiments (DoEs). The resulting SME and the temperature at the die are essential data points needed to indirectly qualify the degradation of the API, which should be minimal. NIR-spectroscopy is used to monitor the material during the extrusion process. In contrast to most applications in which the probe is directly integrated into the die, the optical sensor is integrated into the cooling line of the strands. This saves costs in the probe design and maintenance and increases the robustness of the chemometric models. Finally, a process measurement system is installed to monitor and control all of the critical attributes in real-time by means of first principles, DoE models, soft sensor models, and spectroscopic information. Overall, the process is very robust as long as the screw speed is kept low.