Raw Material Variability Research Articles

Combine wheat and legumes, from the field to soft cakes: raw material variability / process modification / reverse engineering

Combine wheat and legumes, from the field to soft cakes: raw material variability / process modification / reverse engineering

Towards a novel continuous HME-Tableting line: Process development and control concept

The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitude, impact energy and shear viscosity. The complete model is then deployed within a model-based control scheme that facilitates specifying on-spec products via limits on the quality variables. Further, this framework is demonstrated to be capable of rejecting raw material variability to achieve the desired specifications. To replicate raw material variability observed in practice, in this work, the raw material is obtained by blending the matrix resin with a resin of slightly different viscosity at varying weight fractions. Results obtained from experimental studies demonstrate the capability of the proposed model predictive control (MPC) in meeting process specifications and rejecting raw material variability.

Mining and processing of the Stránská skála-type chert during the Late Neolithic and Early Eneolithic periods

Stránská skála represents an important geological and archaeological site. It is also known for its rich deposits of Jurassic cherts that were utilized in various ways during different stages of Moravian prehistory. The substantial Early Upper Paleolithic occupation events are of great significance and have been subject to several international research projects. Archaeological excavations targeting mining and workshop areas were resumed in the last years with a focus on Late Neolithic and Early Eneolithic activities. This paper introduces this project and summarizes recent results. Trends in the chipped industry within the Stránská skála workshop area are outlined, focusing on raw material variability, technology-typology, chronology, and socioeconomic aspects. The first indicators of the location and other aspects of post-Paleolithic mining activities in the field of Stránská skála III are introduced and discussed in the context of mining and workshop activities of similar contemporary features in the Czech Republic and neighboring regions.

Consequences of trace metal variability and supplementation on Chinese hamster ovary (CHO) cell culture performance: A review of key mechanisms and considerations.

Trace metals are supplied to chemically-defined media (CDM) for optimal Chinese hamster ovary (CHO) cell culture performance during the production of monoclonal antibodies and other therapeutic proteins. However, lot-to-lot and vendor-to-vendor variability in raw materials consequently leads to an imbalance of trace metals that are supplied to CDM. This imbalance can yield detrimental effects rooted in several primary mechanisms and pathways including oxidative stress, apoptosis, lactate accumulation, and unfavorable glycan synthesis. Recent research endeavors involve supplying zinc, copper, and manganese to CDM in excess to further maximize culture productivity and product quality. These treatments significantly impact critical quality attributes and furthermore highlight the degree to which trace metal availability can affect CHO cell culture performance. This review highlights the role of trace metal variability, supplementation, and interplay on key cellular mechanisms responsible for overall culture performance and the production and quality of therapeutic proteins.

A new hybrid modeling and optimization algorithm for improving carbon efficiency based on different time scales in sintering process

Sintering process is a major energy-consumption process in steel making processes. Carbon efficiency, which reflects the level of the energy-consumption, is affected by the raw material variables and the operating variables. This situation makes the process with special dynamic characteristics, especially with different operation periods. However, there is seldom research on carbon efficiency with the multiple time-scale property taken into account. This paper introduces an optimization method for carbon efficiency based on an intelligent multiple time-scale model, which is able to optimize process variables in both long and short time scales. As the comprehensive carbon ratio (CCR) and the ratio between CO and CO2 in exhaust gas (CO/CO2) are taken as the carbon efficiency indexes, a predictive model consisting of two sub-models is developed, one for predicting the state variables with a single neural network (NN), and the other for predicting carbon efficiency indexes with a linear combination of NNs. Then a multi-objective, multi-time-scale optimization framework is designed, which is able to optimize carbon efficiency in two time scales, according to the optimization variables available encountered at different operation periods. Finally, the experimental results based on actual process data shows its feasibility and improvement in carbon efficiency optimization.

Managing API raw material variability in a continuous manufacturing line - Prediction of process robustness.

Many studies on continuous twin-screw granulation only focus on the granulator without linking this process step to the upstream and downstream unit operations. Product critical quality attributes (CQAs) are however not only determined by the granulation step. In this study, the possibility to manage the batch-to-batch variability of an active pharmaceutical ingredient (API) in a high drug loaded formulation on a continuous line was investigated to obtain consistent tablet CQAs. As the ultimate goal of continuous manufacturing is to produce 24/7, current study also aimed at guaranteeing long term stability of the process. To do so, previously identified API critical material attributes (CMAs) were varied together with granulation, drying and milling critical process parameters (CPPs) in a screening design of experiments to understand the influence of these factors upon product CQAs and process stability. To evaluate the factors affecting the process stability with a reduced amount of materials, process deviations recorded by process sensors were used. While product CQAs only depended on process CPPs, process stability was strongly affected by API CMAs. The effect of API batch-to-batch variability on process stability could nonetheless be managed by applying suitable granulation conditions. Therefore, appropriate ranges of CPPs were defined to ensure both product CQAs and process stability. By studying the fully integrated continuous manufacturing line, it was possible to highlight the interactions between the different unit operations and the API CMAs and to design a robust process.

Development and validation of in-line near-infrared spectroscopy based analytical method for commercial production of a botanical drug product

Near-infrared (NIR) spectroscopy is one of the most successful pharmaceutical process analytical tools. For botanical drug products (BDPs), many studies have applied NIR spectroscopy for rapid analysis of botanical raw materials, extracts and formulations. However, the real-time process analysis reported for BDPs was still mainly conducted on lab- or pilot- scale equipment, where some essential conditions of the materials and process parameters can be easily controlled for NIR spectral measurement. Due to the chemical and physical characteristics of the commercial production of BDPs, it is challenging to develop in-line NIR methods with enough robustness for industrial-scale application. This is the first reported case study of the development and validation of the in-line NIR method for the commercial production of BDPs, taking Compound Danshen Dripping Pill (CDDP) as an example. An in-line NIR method was developed for simultaneous measurement of the three critical quality attributes, i.e. the relative density, the moisture content and the content of 3,4-dihydroxyphenyl lactic acid (danshensu, a key active compound), during the extract concentration process. The NIR spectra and sample collection lasted for three years (67 production batches) to cover the variability of raw materials and process conditions. NIR calibration models were established respectively, with determination coefficients (r2) of 0.9905, root mean square errors of prediction (RMSEP) of 0.004 for the relative density in the range of 1.042–1.184; r2 of 0.9870, RMSEP of 1.1% for the moisture content in the range of 50.8–83.0%; r2 of 0.9870, RMSEP of 0.461 mg/g for danshensu in the range of 2.563–8.869 mg/g. Then, all the method validation parameters (accuracy, precision, range, specificity, linearity, robustness, detection and quantitation limits) were discussed according to the characteristics of the commercial production of BDPs. The NIR method development and validation strategy proposed may also be applied in the future for the commercial production of other BDPs.

PEMANFAATAN LIMBAH CANGKANG BIJI KARET DENGAN ADITIF TEMPURUNG KELAPA SEBAGAI BAHAN BAKAR ALTERNATIF MELALUI TEKNOLOGI PEMBRIKETAN

The development of alternative energy sources that can replace fuel oil is very important to utilize natural resources optimally and environmentally. The shell produced from rubber plants is the main ingredient in this study, while the coconut shell is an additional material used to increase the calorific value of alternative fuels which is often referred to as Briquette. The purpose of this study is to determine the effect of variations in raw material composition and variations in pressure on the quality of rubber shells and coconut shell waste briquettes according to SNI standards. The raw material for rubber shell and coconut shell is processed into charcoal using carbonization method with a variation of a mixture of 85%: 15%, 90%: 10% and 95%: 5% with 5% adhesive. Then mix the printed material with a pressure of 300 kg/cm2 and 100kg/cm2. The quality parameters of briquettes are based on SNI 01-6235-2000 standards with moisture content, ash content, volatille matter content, and lacquer value. The results of the study showed that the sample b1 with 85% injection: 15% print pressure 300kg/cm2. The briquette with the sample code b1 has a water content value of 5,10432%, ash content of 14,8604%, volatile matter content of 12,8002%, carbon value of 66,8225% and heating value of 6576.592501 cal/gr. But overall the briquettes have not met the standards of SNI 01-6235-2000 concerning the quality of wood charcoal briquettes. Because the ash content of the briquette exceeds the maximum limit that has been determined, namely a maximum of 8%. Keywords: Alternative Energy, Rubber Seed Shell, Coconut Shell, Pressure

Managing API raw material variability during continuous twin-screw wet granulation

Very few studies have investigated the impact of raw material variability upon the granule critical quality attributes (CQAs) produced via twin-screw wet granulation (i.e., granule size distribution, density, flowability). In this study, the impact of the raw material variability of an active pharmaceutical ingredient (API) in a high dose formulation on the twin-screw wet granulation process and on the resulting granule quality attributes was investigated. In a previous study (Stauffer et al., 2018), eight API batches were characterized to determine the API batch-to-batch variability. Principal component analysis (PCA) was then used to analyse the raw material property differences between the API batches and to determine the causes of the batch-to-batch variability. In current study, the three principal components from that PCA model were used as factors together with twin-screw granulation process parameters (i.e., screw speed and liquid-to-solid ratio) in a D-optimal screening design of experiments to understand the influence of these factors upon the granule CQAs. It was found that the API particle size distribution and related properties (e.g., density, agglomeration profile) were critical for the granule CQAs. In a next step, the significant factors from the screening design results were used to determine the design space of the twin-screw granulation process for the studied formulation via a D-optimal optimisation design, herewith controlling the risk of failure for the potential API raw material variability. The possibility to obtain suitable granule CQAs with a risk of failure of 1% for all API batches was demonstrated. It was thus possible to identify a combination of process parameters that can manage the API batch-to-batch variability leading to granules with pre-defined suitable CQAs.

Optimal operation of parallel dead-end filters in a continuous bio-based process

Dead-end cake filtration as one of the first unit operations deployed in biochemical production plants has been subject of academic investigation for many decades. The recurrent discrete reinitialisation events are challenging from a process control point of view, especially in a continuous downstream line. The complexity that arises when multiple units are operated in parallel seems to have received little attention. This work aims at illustrating this complexity and delineates the arising plantwide control problem. Guidelines for optimised operation are derived from general process understanding and at hand of an industrial case study. The need for a predictive model to solve multiple scheduling problems is identified, and a mathematical model based on conventional filtration theory is derived. Due to raw material variability and operational uncertainties, the predictions are found to be too imprecise for deployment. This is expected to be representative of many bio-based processes, where manual scheduling needs to be integrated effectively into plantwide control structures.

Population convergence in the Strobel Plateau: A discussion based on the study of lithic raw materials variability

Rock art research carried out in the Strobel Plateau (Santa Cruz province, Patagonia, Argentina - 700 to 1200 masl) has shown that during Late Holocene population convergence took place in this area, where information was concentrated and distributed on a supra-regional scale (Belardi & Goñi 2006; Goñi 2010; Re 2010; Goñi et al. 2007, 2014). The central position of this plateau in the province's steppe would also be a crucial factor regarding this process (Belardi & Goñi 2006). In this context, the aim of this work is to study this idea of population dynamics given the evidence from lithic technology. In order to do this, we first consider the analysis of variability and provenance of lithic raw materials used for the manufacture of artefacts. It is important to highlight that after 15 years of research carried out in the Strobel Plateau we have not found any lithic source. However, previous works suggest that lithic resources used in the Strobel Plateau are located in nearby areas. Therefore, when analysing the variability, provenance and distance of sources, this work seeks to discuss the access to the plateau and the regional circulation axes. The obtained results support the idea of population convergence occurring in Late Holocene.

Taking into Account Upstream Variability of Flours with Processing Variables in Legume-Enriched Soft Cakes: Conception of a Multiobjective Model for the Monitoring of Physical Properties

The use of composite wheat-pea flours is of nutritional interest because their essential amino acid profile is closer to human needs than that of wheat alone. However, composite flours might be more variable than wheat flour alone, so manufacturers need to be able to monitor the quality of the cakes. The effects of the quality of the raw material and cake manufacturing process variables on cake density and shape and crumb color, texture, and cell structure were studied. An experimental design with seven flour quality and processing variables was implemented (56 trials). A multiobjective model was built, and its prediction capacity was validated. It is able to compensate for variations in flour quality by adjusting the levels of the processing variables.

Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet.

In the pharmaceutical process, raw material (including APIs and excipients) variability can be delivered to the final product, and lead to batch-to-batch and lot-to-lot variances in its quality, finally impacting the efficacy of the drug. In this paper, the Panax notoginseng saponins (PNS) sustained-release matrix tablet was taken as the model formulation. Hydroxypropyl methylcellulose with the viscosity of 4000mPa·s (HPMCK4M) from different vendors and batches were collected and their physical properties were characterized by the SeDeM methodology. The in-vitro dissolution profiles of active pharmaceutical ingredients (APIs) from matrix tablets made up of different batches HPMC K4M displayed significant variations. Multi-block partial least squares (MB-PLS) modeling results further demonstrated that physical properties of excipients played dominant roles in the drug release. In order to achieve the target drug release profile with respect to those far from the criteria, the optimal selection method of incoming materials from the available was established and validated. This study provided novel insights into the control of the input variability of the process and amplified the application of the SeDeM expert system, emphasizing the importance of the physical information of the raw materials in the drug manufacturing process.

Designing Robust Tablet Formulas Resilient to Raw Material and Equipment Variations with the Aid of Design of Experiment (DOE) Method

In tablet production, the physical properties of tablet products are found to change from batch to batch occasionally, which could be caused by raw material and equipment variations. This article focuses on how to design robust formulas resilient to raw material and equipment variations with the aid of Design of Experiment (DOE) method, how to optimize the concentrations of each individual ingredient, and how to avoid the common production issues related to tablet capping, chipping, and dusting problems. The impacts of particle sizes and equipment on compression behaviors were experimentally evaluated and explained with a theoretical hypothesis. The main goal is to show how tablet product quality is controlled from fundamental scientific principles when many active ingredients are present in a single formula, hard to be engineered and dramatically different from the pharmaceutical products. Our findings may provide insightful clues on tablet formulations and design strategies.

Aspen Plus Simulation Strategies Applied to the Study of Chitin Bioextraction from Shrimp Waste.

SUMMARYChitin is an aminopolysaccharide of industrial interest commonly obtained from shrimp processing waste through chemical or biotechnological means. Current environmental concerns offer a stimulating perspective for chitin bioextraction with lactic acid bacteria since a considerable reduction in the use of corrosive and pollutant products is possible. Nevertheless, the efficiency of this bioprocess is still a matter of discussion. In this work, the experimental studies of chitin bioextraction from Pacific white shrimp (Litopenaeus vannamei) waste with a mixed culture of Lactobacillus plantarum, Lactobacillus bulgaricus and Streptococcus thermophilus are used in process simulation using Aspen Plus software for the analysis of the potential application of a bioprocess on plant scale. The experimental results of characterization in shake flasks and 1-litre bioreactor indicated that 50 h of fermentation with the mixed culture of lactic acid bacteria was enough to extract more than 90% of minerals and proteins from the shrimp waste. The use of experimental parameters in the simulation allowed a reliable representation of the bioprocess yielding normalized root mean square values below 10%. Simulation was used for the assessment of the impact of the raw material variability on the production costs and gross margin. In this regard, the gross margin of the operation ranged from 42 to 52% depending on the raw material composition and product yield.

Managing active pharmaceutical ingredient raw material variability during twin-screw blend feeding

Continuous powder feeding is a critical step in continuous manufacturing of solid dosage forms, as this unit operation should ensure the mass flow consistency at the desired powder feed rate to guarantee the process throughput and final product consistency. In this study, twin-screw feeding of a pharmaceutical formulation (i.e., blend) existing of a highly dosed very poorly flowing active pharmaceutical ingredient (API) leading to insufficient feeding capacity was investigated. Furthermore, the API showed very high batch-to-batch variability in raw material properties dominating the formulation blend properties. Formulation changes were evaluated to improve the flowability of the blends and to mitigate the impact of API batch-to-batch variability on the twin-screw feeding. Herewith, feeding evaluation tests and an extensive material characterization of the reformulated blends were performed to assess the impact of the formulation changes upon continuous twin-screw feeding. The transfer of the glidant from extra-granular to intra-granular phase allowed to improve the flowability of the blends. A sufficient feeding capacity for the downstream process and a mitigation of the impact of batch-to-batch variability of the API upon twin-screw feeding of the blends could be achieved. No effect of the formulation or of the API properties on the feeding stability was observed. The material characterization of the blends allowed identifying the material attributes which were critical for continuous twin-screw feeding (i.e., bulk density, mass charge and powder cohesiveness).

Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to formulation performance

A key part of the Risk Assessment of excipients is to understand how raw material variability could (or does) contribute to differences in performance of the drug product. Here we demonstrate an approach which achieves the necessary understanding for a complex, functional, excipient. Multivariate analysis (MVA) of the certificates of analysis of an ethylcellulose aqueous dispersion (Surelease) formulation revealed low overall variability of the properties of the systems. Review of the scores plot to highlight batches manufactured using the same ethylcellulose raw material in the formulation, indicated that these batches tend to be more closely related than other randomly selected batches. This variability could result in potential differences in the quality of drug product lots made from these batches. Manufacture of a model drug product from Surelease batches coated using different lots of starting material revealed small differences in the release of a model drug, which could be detected by certain model dependent dissolution modeling techniques, but they were not observed when using model-independent techniques. This illustrates that the techniques are suitable for detecting and understanding excipient variability, but that, in this case, the product was still robust.

Unmanned machine vision system for automated recognition of mechanical parts

PurposeThe field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.Design/methodology/approachThe overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.FindingsClassification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.Originality/valueAfter calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

Setting up multivariate specifications on critical raw material attributes to ensure consistent drug dissolution from high drug-load sustained-release matrix tablet

The purpose of this study was to describe the raw material variability that influenced the in-vitro dissolution behavior of high drug-load sustained-release matrix tablet and to ensure the consistent quality of the final product. The Panax notoginseng saponins (PNS) – hydroxypropyl methylcellulose – anhydrous lactose – magnesium stearate (57:20:23:0.5%, w/w) was used as the model formulation. PNS extract powders with lot-to-lot and source-to-source differences were collected to cover the common cause variations and their physicochemical properties were characterized by the chromatographic fingerprints and the SeDeM expert system. It was found that the release behavior of active pharmaceutical ingredients (APIs) in PNS from different batches exhibited considerable variations. Latent variable modeling results demonstrated that the physical properties of raw materials played major roles in predicting the drug dissolution. PNS extracts with high specific surface area, the width of particle size distribution and hygroscopicity or low moisture content led to an increase in drug release. In order to perform efficient pass/fail judgments for incoming new materials, multivariate specifications of critical material attributes (CMAs) were established and the multivariate design space in line with the quality by design (QbD) principles was explored to achieve the release target.