Raw Material Variability Research Articles

Real-time adjustment of injection molding process settings by utilizing Design of Experiment, time series profiles and PLS-DA

Various production/process equipment’s have built-in sensors allowing for continuous collection of process data. However to ease the data processing burden, it is often the case that only certain features such as aggregated measures or peak values are stored. Yet also in some cases such sensor signals can be extracted fully reflecting the dynamics of the process and utilized for process optimization. The aim of this paper is to demonstrate that such data can be utilized in an effective way to optimize an injection molding process using signals from built-in pressure and position sensors. The observational process data is combined with data from controlled experiments to observe the causal relationships between disturbance factors, process settings and the final quality of the products. We demonstrate that signals from built-in injection molding machine sensors can be used for detecting and mitigating quality issues caused by variation in raw material due to the dual sourcing from two suppliers, which cannot actually be identified during production. For this, we show that the origin of raw material can be classified using the time series profiles of dosing pressure and PLS-DA (Partial Least Squares Discriminant Analysis). Through experimental work, we conclude that this classification can be used for increasing the operating window for holding pressure and mold temperature, which ensure production of products within specifications.

Prediction of entire tablet formulations from pure powder components’ spectra via a two-step non-linear optimization methodology

Process analytical technology in the pharmaceutical industry requires the monitoring of critical quality attributes (CQA) through calibrated models. However, the development, implementation, and maintenance of these quantitative models are both resource and time-intensive. This study proposes the implementation of a non-linear iterative optimization technology (IOT) to study the magnitude of analytical errors when the calibration tablet used to extract the λ vector deviates physically and chemically from the test samples. IOT is based on mathematical optimization of excess spectral absorbance. It requires minimum calibration effort and allows simultaneous prediction of the entire formulation instead of only the active pharmaceutical ingredient (API), with just one standard and pure component spectral data. Unlike Partial Least Squares (PLS), which requires the development of standards to incorporate variations in the process, this non-destructive methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. The method described in this study allows a fast re-calculation to include factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet preparation. The robustness of the proposed approach for variation in compaction (physical changes) and variation in composition (chemical changes) was evaluated for correlated and uncorrelated formulations. For uncorrelated formulation a PLS model was also constructed to compare the robustness of the proposed methodology. The RMSEP of API in target formulation predicted using non-linear IOT method was varied from 0.17 to 1.50 depends on compaction of tablet chosen to compute λ vector. On the other hand, the RMSEP of API in target formulation predicted using PLS-based model was varied from 0.13 to 0.57 depending on compaction of tablet. The additional accuracy achieved in PLS based model required significant calibration effort of preparing 84 tablets compared to just one in proposed non-linear IOT method.

Investigating changes in lithic raw material use from the Middle Paleolithic to the Upper Paleolithic in Jebel Qalkha, southern Jordan

Although the Middle-Upper Paleolithic transition in the Levant is known for changes in multiple aspects of archaeological records such as lithic technology, subsistence practices, and the use of ornaments, little is known about whether any change occurred in lithic raw material use over this period. To examine this underexplored issue, this paper presents an analysis of raw material (chert) variations in four lithic assemblages that represent the Late Middle Paleolithic (LMP), the Initial Upper Paleolithic (IUP) and the Early Upper Paleolithic (EUP) in the Jebel Qalkha area, southern Jordan. To characterize chert variation, we used a novel method for the quantification of surface roughness in addition to more conventional macroscopic attributes, such as color and translucency. To evaluate the validity of the new measurement of surface roughness, we conducted petrographic and geochemical examinations to show their correlations with ratio-scale data (Ra values) of the surface roughness. As a result, we established three major chert types (Types M, FL and FH) based on two macroscopic criteria (texture and translucency) that correlate with microfossil contents and major chemical compositions. The relative frequencies of the three chert types in the four lithic assemblages showed an increase in Type FH in the EUP assemblage. This indicates that a clear change in lithic raw material was observed not at the classical boundary between the MP and UP, but between the IUP and EUP. This result is discussed in relation to the development of bladelet technology in the EUP and its possible relevance to the selection of raw materials.

Az élelmiszeripar helyzete és fejlesztési irányai

The food industry in Hungary has come a long way from the 1800s to the present day and is still facing many challenges. It is a highly heterogeneous sector with a large number of employees, whose uniqueness and specificities have a significant impact on its present and potential. Its development potential lies in increasing added value, moving towards quality products, and positioning itself on domestic and export markets. The pace of development is determined by strategies, subsidies, the regulatory environment, R&D&I opportunities, consumer needs and related education, the availability and variability of raw materials, strict food safety requirements, etc. In this article, we present the past, present and development trends of the food industry based on the lecture 100+10 years of food science at the 240th anniversary of the Budapest University of Technology on 19 November 2021.

Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion

Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.

Middle Palaeolithic, Transitional or Upper Palaeolithic: Geoarchaeological revision of the southern part of the loess site Dzierżysław 1, SW Poland

Sites identified as the Early Upper Palaeolithic (EUP or transitional industries) in Central Europe (CE) have become subject to extensive discussion on the possible interactions between anatomically modern humans and indigenous human species which inhabited the area in the Marine Isotopic Stage 3 (MIS 3, 50–35 ka BP). An open-air site Dzierżysław 1 (SW Poland), known for nearly a hundred years, played a crucial role in envisaging scenarios associated with the development and adaptation of the EUP society. In the late 20th c., due to the excavations in the southern part of the site, it was given status of a multicultural site, with artefacts assigned to the Micoquian, Bohunician and Szeletian culture.This paper presents the findings of the multi-faceted revision of this site. We present the results of field work and chronometric analyses based on a new set of samples and archaeological study of archive records. The use of optically stimulated luminescence (OSL) showed that all the sediments of the site should be assigned to the Plenivistulian (MIS 4/3 and MIS 2). The new chronometric assessment indicates the absence of deposits corresponding to the transitional industries' time window. The sedimentological data integrating with spatial distribution of finds and raw material variation as well as state of preservation and typo-technological features of lithic artefacts indicate that the inventory represents a compilation of the late Middle Palaeolithic (MP, Micoquian) and classical Upper Palaeolithic industries (Aurignacian or Gravettian) with very few pieces corresponding with indeterminate EUP industry.The revision throws a light on the situation in the Moravian Gate and areas north of the Carpathians and Sudetes. The results suggest that sites from the late MP are more numerous than those from the EUP. It seems that the over-representation may result from a long time of accumulation during the MP period. It cannot be excluded that the small number of EUP sites reflects a marginal role of the area north of the Sudetes and Carpathians in the human occupation system of that time.

Effects of poultry raw material variation and choice of protease on protein hydrolysate quality

The aim of this study was to investigate the role of compositional variations in side-streams from the chicken and turkey industry on product characteristics after enzymatic protein hydrolysis (EPH). Variation was obtained using four raw materials, namely chicken and turkey carcasses, and mechanically deboned chicken and turkey residues. Alcalase 2.4 L, Corolase 2TS and Flavourzyme, three commercial proteases with different specificities, were used to investigate impact of protease choice on product characteristics. Besides protein yield, effects on product characteristics such as nutrient and amino acid composition, size distribution of peptides, degree of hydrolysis (DH%), rheological properties, and angiotensin-I-converting enzyme (ACE) inhibition, were investigated. Results show that choice of protease and industrially relevant variations in poultry raw material composition both have major effect on product composition and the other quality parameters studied. Protein yield, proximate composition, and bioactivity were more affected by protease than raw material variation. Raw material variation had a bigger impact on quality parameters such as peptide size distribution and amino acid composition, exemplified by the sum of essential amino acids. This means that raw material variation should be handled and accounted for to realize the true potential that exists in tailor-making EPH products with specified properties for targeted applications.

Pembuatan Batubata dengan Bahan Baku Lumpur Sidoarjo

Many attempts to reduce the volume of Sidoarjo mudflow that came out, one of which is the use of Sidoarjo mud for making bricks. The results of research in the chemical laboratory show that the Sidoarjo mud mineral content is mostly silica and alumina minerals, so it is very supportive for use as a material for making bricks. Making bricks requires mixed materials, one of which is paper sludge. Paper sludge waste is discharged from the paper industry, which can pollute the environment if it is discharged directly into the environment. Therefore, a solution is needed by utilizing the potential of the sludge waste. One of them is by using paper sludge as an additive in making bricks, because most of the components of paper sludge waste are calcium carbonate. In building materials, calcium carbonate can function as an additive so that paper sludge waste can be used as an additional material in making bricks. The purpose of this study is to determine the best ratio of Sidoarjo mud with paper sludge to the quality of the bricks, find out the best combustion temperature to the quality of the bricks produced, and compare the quality of the bricks before and after burning. The bricks are synthesized with a variation of the ratio of Sidoarjo mud raw material and paper sludge (1: 0, 1: 1, 2: 1, 3: 1) and variations in combustion temperature (no combustion, 400°C, 450°C, 500°C, 750°C, 900°C). In this study, the best quality of bricks according to SNI is 2: 1 ratio bricks with a combustion temperature of 750 ° C. The ratio of 2: 1 brick with a combustion temperature of 750°C has a percentage of water absorption of 17.17% and compressive strength of 37.78 kg / cm2.

Techno-Economic Analysis on the Production of Copper Oxide Nanoparticles by Green Synthesis Method using Abultion indicum Leaf Extract on an Industrial Scale

The purpose of this study was to evaluate the economic feasibility of producing copper oxide (CuO) nanoparticles using the green synthesis method on an industrial scale for 10 years by evaluating from an engineering and economic perspective. Various economic parameters are used to analyze economic viability, including Gross Profit Margin (GPM), Cumulative Net Present Value (CNPV), Payback Period (PBP), as well as economic variations in sales, taxes, raw materials, labor wages, and utilities to ensure project feasibility. Technical analysis to produce 100 kg of CuO nanoparticles per day requires a total production cost of 220,322.850.00 IDR and the gross profit margin is 16,721,250.000,00 IDR per year. PBP analysis shows that the investment will be profitable after more than three years. This project can compete with PBP capital market standards because of the short investment return. The profit is relatively economical, so this project can be run for 10 years under ideal conditions. This research is expected to be a reference for technical and economic analysis of industrial-scale production of CuO nanoparticles

Industrial objects recognition in intelligent manufacturing for computer vision

PurposeThe 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.Design/methodology/approachComputer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. 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. In this work, recognition of objects manufactured in mechanical industry is considered. 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 mechanical part. Red, green and blue 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 as well as the effect of learning rate and momentum.FindingsOne important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.Originality/valueThis paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. 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 as well as the effect of learning rate and momentum.

An agile and robust in-line NIR potency deviation detection method for monitoring and control of a continuous direct compression process

Near Infrared (NIR) method for blend potency estimation has been commonly used as an essential tool for process monitoring and control in continuous manufacturing of solid oral dosage forms. Robustness has been the main challenge for successful application of an NIR method, which often results in a long development time with frequent method update. Robustness deficiency often presents as an offset (bias) on the mean potency estimation. In this paper, the purpose of the NIR method has been redefined from estimating potency to potency deviation. This quantitative approach uses the mean centered potency to estimate potency deviations from the process mean, therefore, detects the non-conforming materials for continuous process monitoring and control.An NIR method was developed at the lab benchtop scale and directly deployed to a direct compression continuous manufacturing platform at Pfizer for mean centered potency estimation. The benchtop calibration provided a speedy and efficient NIR method development and the method showed enhanced robustness for estimating potency deviation in presence of wide process and raw material variations. Integrating with the mean centered approach, the NIR model from the lab could be implemented to different sites using different instruments without requiring model update for the established range of process conditions and raw material properties.

Continuous twin screw granulation: Impact of microcrystalline cellulose batch-to-batch variability during granulation and drying – A QbD approach

Despite significant advances in the research domain of continuous twin screw granulation, limited information is currently available on the impact of raw material properties, especially considering batch-to-batch variability. The importance of raw material variability and subsequent mitigation of the impact of this variability on the manufacturing process and drug product was recently stressed in the Draft Guidance for Industry on Quality Considerations for Continuous Manufacturing by the U.S. Food and Drug Administration (FDA). Therefore, this study assessed the impact of microcrystalline cellulose (MCC) batch-to-batch variability and process settings in a continuous twin screw wet granulation and semi-continuous drying line. Based on extensive raw material characterization and subsequent principal component analysis, raw material variability was quantitatively introduced in the design of experiments approach by means of t1 and t2 scores. L/S ratio had a larger effect on critical granule attributes and processability than screw speed and drying time. A large impact of the t1 and t2 scores was found, indicating the importance of raw material attributes. For the studied formulation, it was concluded that MCC batches with a low water binding capacity, low moisture content and high bulk density generated granules with the most desirable quality attributes. Additionally, an innovative and quantitative approach towards mitigating batch-to-batch variability of raw materials was proposed, which is also applicable for additional excipients and APIs.

Considerations for controlling the variability of raw biological materials in the manufacturing of autologous patient-specific therapies

Considerations for controlling the variability of raw biological materials in the manufacturing of autologous patient-specific therapies

Event-based dynamic optimization for food thermal processing: High-quality food production under raw material variability

Industrial canneries are subject to perturbations that may compromise food safety requirements. In such cases, plant operators typically increase the processing time, leading to undesirable large processing cycles and excessive quality degradation. In addition, differences among the items in a batch lead to variability in terms of quality and safety which, if not explicitly considered in the processing strategy, forces the use of conservative operation policies.In this work, we present an event-based dynamic optimization approach that combines available plant measurements and mathematical model predictions to anticipate the effect of plant perturbations on food safety. A safety software sensor is build upon an on-line predictive simulation and a previous food-variability characterization such that, if any perturbation during the sterilization compromises food safety, a new processing strategy that optimizes a trade off among quality, uniformity and processing time is recomputed and implemented. Such multi-objective dynamic optimization problem under food product variability is efficiently addressed by taking advantage of the monotonicity and convexity properties of the food quality/safety dynamics.

Effect of Spray Drying Conditions on Physical Properties of Panax notoginseng Saponin (PNS) Powder and the Intra-Batch Dissolution Variability of PNS Hydrophilic Matrix Tablet.

PurposeUnderstanding raw material variability and its impact on product quality are crucial for developing robust pharmaceutical processes. This work aimed to study the effects of spray drying conditions on properties of the spray dried Panax notoginseng saponin (PNS) powders as well as the subsequent intra-batch dissolution variability of PNS hydrophilic matrix tablets.MethodsThe Plackett-Burman design was applied to screen the critical process parameters (CPPs). Then, the Box-Behnken design was used to investigate the relationship between the CPPs and the physiochemical properties of spray dried PNS powders. The PNS hydrophilic matrix tablets containing 57% spray dried PNS powders were directly compressed. The partial least squares (PLS) regression was used to uncover the hidden multivariate relationships among the CPPs, intermediate powder properties, and tablet quality attributes.ResultsThe identified CPPs were the feed concentration, the inlet air temperature, and the atomization pressure. It was found that the CPPs exerted little impact on chemical properties of spray dried PNS powders, but had significant impact on physical properties, such as particle size, specific surface area, bulk density, hygroscopicity, and inter-particle porosity, etc. Latent variable modeling results revealed that the high inlet air temperature of spray drying process could produce PNS powders with low moisture content and high hygroscopicity, which were beneficial to reduce the intra-batch dissolution variability of PNS hydrophilic matrix tablets. Finally, a design space of the spray drying process was built in order to ensure the dissolution consistency.ConclusionOur research provided a reference for improving the spray drying conditions in order to ensure the dissolution consistency of the PNS hydrophilic matrix tablet.

Plants used in basketry production during the Early Neolithic in the north-eastern Iberian Peninsula

Plant-based artefacts are rarely preserved in archaeological contexts in the Iberian Peninsula. Very few sites with specific conditions such as waterlogging, aridity or carbonization have preserved organic material. The aim of this paper is to establish an identification criterion for identifying organic raw materials used for manufacturing plant-based artifacts by using optical microscopy. The coiled basketry remains found in La Draga (Banyoles, Girona) and Coves del Fem (Ulldemolins, Tarragona) have been studied using this method. The materials were exceptionally well-preserved by waterlogging in the case of La Draga, and carbonized or dehydrated in Coves del Fem. Samples of both coils and stitches from all the basket remains were analysed. The anatomy of their cross-sections was observed under a transmitted light bright-dark field (BF-DF) optical microscope and compared with modern reference collections. The results show the use of at least five species corresponding to four families: three monocotyledons (Poaceae, Cyperaceae and Typhaceae) and a dicotyledon (Malvaceae). Moreover, it has been possible to distinguish the part of the plant used. Variability in raw materials implied extensive environmental knowledge. Moreover, functional studies of wooden, faunal, lithic and malacological tools suggest a local production of baskets. This paper expands the current knowledge of raw materials used for basketry purposes during prehistory in the Mediterranean area as the examples from La Draga and the Coves del Fem are the oldest basketry remains that have been studied in depth in the Iberian Peninsula and Europe.

The Effect of Raw Material on the Identification of Knapping Skill: a Case Study from Olduvai Gorge, Tanzania

The identification of Oldowan hominin knapping skill levels has been a focus of numerous studies, with apparent variation in technical abilities identified between a number of Early Stone Age archaeological sites. Raw material variability, however, can play a significant role in the outcomes of knapping events as well as in the accuracy of analysis. Implications of such variability are yet to be fully understood. Here we present an experimental study to assess the effects that varying raw materials have on the identification of technological attributes typically associated with varying skill levels and whether it is possible to identify knapper skill levels across multiple raw materials. Variation was tested between raw materials from Olduvai Gorge across and between skill levels. The results suggest that knapping skill levels manifest differently in the material record across raw materials. In addition, we suggest that raw material has a significant effect on identifying knapper skill variation. This has implications for future research concerned with identifying knapper skill within and between early assemblages of differing raw materials.

Comparative Pharmacognostical Evaluation of Five Different Market Samples of Piper Longum Linn.

Pippalimoola (root of Piper longum.linn) is one of most important drug in Ayurveda used as single drug and in combination with other herbs ,minerals or metallic preparations for poly herbo-mineral formulations.. In Ancient literature we found that in the name of ‘Moola’(means firmly fixed a root) we must practice root but now a days instead of Pippali moola most of market samples are substituted with stem of same plant, so it is essential to check the botanical source of market samples.In this study,different five market samples of Pippalimoola were procured all over India and one sample (control) was collected after authentication of botanical source of plant.Pharmacognostical study was undertaken to observe morphological characters of market samples and compared with the collected sample. It was observed that out of five market samples; macroscopic and microscopic characters of one sample was matched with the characters of control root sample. While other four samples were found to be stem of the plant. Hence there is lots of variation in raw material of the plant in Indian market.

From near and far: Stone procurement and exchange at Çukuriçi Höyük in Western Anatolia

The focus of this paper are the stone tools of Çukuriçi Höyük, a prehistoric site situated at the central Aegean coast of Anatolia. The settlement was inhabited from the Neolithic, through the Late Chalcolithic and Early Bronze Age 1 periods, a period lasting from the early 7th to the early 3rd millennium BCE. A long-term interdisciplinary study of the excavated lithics with different scientific methods on various stone materials (thin section analysis, pXRF, NAA, LA-ICP-MS) offer new primary data about the procurement strategies of prehistoric societies from a diachronic perspective. The results will be presented for the first time with an overview of all source materials and their distinct use through time.
 The lithic assemblages from Çukuriçi Höyük consist of a considerable variety of small finds, grinding stones and chipped stone tools. The high variability of raw materials within the different categories of tools is remarkable. In addition to stone tools manufactured from sources in the immediate vicinity of the settlement (i.e., mica-schist, limestone, marble, amphibolite, serpentinite), others are of rock types such as chert, which indicate an origin within the broader region. Moreover, volcanic rocks, notably the exceptionally high amount of Melian obsidian found at Çukuriçi Höyük, attest to the supra-regional procurement of distinct rock types. Small stone axes made of jadeite presumably from the Greek island of Syros, also indicate these far-reaching procurement strategies.
 The systematic and diachronic analyses of the stone tools found at Çukuriçi Höyük has demonstrated that as early as the Neolithic period extensive efforts were made to supply the settlement with carefully selected raw materials or finished goods procured from distinct rock sources.

Estimation of Net Carbon Consumption in Aluminum Electrolysis Using Multivariate Analysis

In the aluminum electrolysis, the amount of carbon consumed per unit of metal produced is always higher than the theoretical one. Raw material variability, anode properties, and reduction cell operation and performance can influence this discrepancy. Linking these factors to each individual anode could help close this gap thus improving the environmental footprint and reducing production cost. In today’s industrial environment, large volume of data, both structured and unstructured, are collected, otherwise known as big data. These capabilities could enable predicting the consumption for each anode as opposed to estimating it at the plant level as performed so far. This can be achieved using a tracking system to link the pot operation to individual baked and green anode properties. The performance of R&D Carbon Net Carbon Consumption formula was compared to a multivariate statistical approach that considers process data variables gathered from the aforementioned, linked, databases. Given the process data at hand, the prediction power of the net carbon consumption for individual anodes is significantly improved as opposed to using the existing model.