Raw Material Variability Research Articles

Life Cycle Assessment of Wood Pellet Production in Thailand

Thailand has increased wood pellet production for export and domestic use. The variations in production processes, raw materials, and transportation related to wood pellet production make it necessary to evaluate the environmental impacts assessment. The objective of this study was to compare via Life Cycle Assessment (LCA), eight different cases of wood pellet production varying in terms of raw materials, production processes, energy use, and the format of transportation and to compare LCA of electricity production from wood pellets and fossil fuels. The comparison results show that leucaena is better as a feedstock for wood pellet production than acacia due to shorter harvest cycle and lesser use of resources. Pellet production consumes the most energy contributing significantly to the environmental impacts. The use of fossil fuels in wood pellet production and transportation also has a major contribution to the environmental impacts. Using wood pellets for electricity production is better than lignite in terms of human health, ecosystem quality and resource scarcity. Recommendations from this study include increasing yield of feedstock plants, shortening harvest cycle, reducing overuse of fertilizers and herbicides, pollution control, reducing fossil fuel use in the supply chain, good logistics, feedstock access, and offering incentives considering the externality cost.

Investigations of Feldspar-Quartz Raw Materials After Firing: Effect of Various Na2O/K2O Ratio and Synthetic Pigments Addition

Different techniques (chemical analysis, X-ray diffraction, Mössbauer spectroscopy, UV-Vis-NIR spectrophotometry and chromaticity measurements) were used to study the color variation of feldspathic raw materials after firing at 1200 °C. Two varieties of feldspar-quartz grits (with Na2O or K2O prevalence) doped with synthetic pigments (hematite and rutile) were heat-treated in an oxidizing atmosphere. The results obtained indicate that the color of the ceramic body after firing may not only depend on the presence and analytical content of coloring elements (such as iron and titanium), but on their valence state and the chemical characteristics of the melt itself. The prevalence of larger alkali K+ ions over Na+ ions may cause the reduction of the coordination number of iron. This has a significant effect on the length of the Fe–O bond, which in turn influences the capacity for light absorption of a specific wavelength. The results of spectrophotometric measurements showed pronounced disparity between the raw materials and samples doped with coloring oxides. The introduction of synthetic pigments caused clear changes in the spectral dependence of the total absorbance. The absorbance increased in the whole measuring range, in particular in the visible part of the spectrum. Furthermore, a red-shift of the absorption bands towards longer wavelengths attributed to oxygen-metal charge transfer (OMCT) interactions between Fe3+ and O2− ions as well as to d-d transitions within Fe3+ ions was observed.

A stochastic approach for integrated production and distribution planning in dairy supply chains

This work addresses production and distribution planning for a real-world dairy supply chain. The planning model accounts for the production and distribution of Cheese, Yogurt, Powdered Milk and UHT milk products across a two-echelon Supply Chain. This task is undermined by the inherent variability of raw materials and finished products demand. The integrated production and distribution planning methodology introduced is based on a two-stage stochastic mixed integer linear programming formulation. In real-world settings the number of scenarios grows substantially; thus, a scenario reduction strategy based on clustering techniques is given. A decomposition and solving strategy is also introduced and applied to a real-world case study. This study showed that the value of the stochastic solution might rise to 21.1% above the deterministic solution. This indicates the importance of considering uncertainty for dairy production and distribution. Besides, different-size instances are tested to study the scalability of the solution approach.

Evaluation of the potential of fireweed (Epilobium angustifolium L.), European goldenrod (Solidago virgaurea L.), and common broom (Cytisus scoparius L.) stems in bioethanol production

AbstractOne of the main goals of industrial biotechnology is to develop an effective method for ethanol production for fuel purposes using lignocellulosic biomass. Variability of lignocellulosic raw materials, selection of an effective method for the pretreatment of raw material, and selection of microorganisms with the ability to ferment not only hexoses but also pentoses and are moreover resistant to environmental stress generated by the products of lignocellulosic complex decomposition, are the challenges encountered in ethanol production. The use of agricultural wastelands and overgrowing plants that have little possibility of application in processes other than energy production seem to be an interesting alternative to conventional, but very often rather cultivation demanding energy crops. The aim of this study was to evaluate the possibility of using the stems of fireweed (Epilobium angustifolium L.), European goldenrod (Solidago virgaurea L.), and common broom (Cytisus scoparius L.) for ethanol production. The key elements studied were characteristics of the lignocellulosic complex structure, influence of the selected ionic liquids on the structural changes in biomass, and efficiency of enzymatic hydrolysis and ethanol fermentation processes. The results showed that under the assumed conditions the best effect was observed with the fireweed materials subjected to pretreatment with 1‐ethyl‐3‐methylimidazolium acetate and enzymatic hydrolysis with Viscozyme® preparation. The final concentration of ethanol obtained was 2.509 g L−1 with a yield of 92.3%. This was due to the highest share of cellulose (40.9%) in the whole lignocellulosic complex compared to other raw materials, which in combination with the selection of an appropriate ionic liquid and an enzymatic preparation, led to high bioprocess efficiency.

Enhanced stability of cell-based products with recombinant human serum albumin in combination with an optimized inclusion of cryoprotective agents in a chemically defined cryoprotective media

Background & Aim The need for a stable final cell product to be delivered to the patient is essential to ensure product efficacy and safety with cellular therapies. It is well known that human serum albumin has critical cryoprotective activity for cell-based products in final formulations that are to be administered to the patient. However, use of plasma-derived albumin can introduce inconsistency through raw material variability which can negatively impact the final cell product efficacy and reproducibility. Recombinant human serum albumin offers an attractive solution to circumvent these challenges that arise from raw material heterogeneity in manufacturing and distribution of cell-based products. Methods, Results & Conclusion Here, we have incorporated a novel recombinant human serum albumin into an optimized chemically defined and blood-free cryopreservation media to enhance the stability of previously expanded T Lymphocytes. Cell viability was measured at multiple time points post-thaw to ensure prolonged stability of the expanded cell product. Cells cryopreserved in the chemically defined media maintained viability over time that was comparable to current industry standard final formulations. Phenotype of the cryopreserved T Lymphocytes was also measured along with the viability post-thaw to confirm cell identity. Altogether, we have demonstrated that recombinant human serum albumin can serve as a cryoprotectant in expanded T Lymphocytes to replace plasma-derived albumin and thwart the problems associated with raw material inconsistency in cellular therapies. The need for a stable final cell product to be delivered to the patient is essential to ensure product efficacy and safety with cellular therapies. It is well known that human serum albumin has critical cryoprotective activity for cell-based products in final formulations that are to be administered to the patient. However, use of plasma-derived albumin can introduce inconsistency through raw material variability which can negatively impact the final cell product efficacy and reproducibility. Recombinant human serum albumin offers an attractive solution to circumvent these challenges that arise from raw material heterogeneity in manufacturing and distribution of cell-based products. Here, we have incorporated a novel recombinant human serum albumin into an optimized chemically defined and blood-free cryopreservation media to enhance the stability of previously expanded T Lymphocytes. Cell viability was measured at multiple time points post-thaw to ensure prolonged stability of the expanded cell product. Cells cryopreserved in the chemically defined media maintained viability over time that was comparable to current industry standard final formulations. Phenotype of the cryopreserved T Lymphocytes was also measured along with the viability post-thaw to confirm cell identity. Altogether, we have demonstrated that recombinant human serum albumin can serve as a cryoprotectant in expanded T Lymphocytes to replace plasma-derived albumin and thwart the problems associated with raw material inconsistency in cellular therapies.

Influence of Raw Material Properties on Parameters of Injection Press during the Injection of Composites Based Biomass and Plastic Waste

The main aim of this paper is to present the research findings which come out from the experimental determination of the influence of input raw material properties and composition on the operating parameters of an injection press during the injection of biomass-plastic composites (BPC). During the injection process, important operating parameters such as injection speed, injection pressure and the temperature profile along the chamber can be recognized. In this research study, the aim was to produce BPCs of an acceptable and competitive level of quality which is determined from the final mechanical properties of BPCs. Using a variety combination of influencing variables the final quality of composites and also the operating parameters of the injection moulding press can be improved. Raw waste material variables influence, especially (a type of plastic matrix, type of biomass, particle size, moisture content) and biomass/plastic concentration ratio can be recognized during the production of composites. Their effect can be seen from the quality indicators and from the operating parameters of the injection press which has a direct impact on the production costs. The paper deals with the determination of the impact and the relationship between the input raw material variables and the operating parameters of the injection process (pressure, speed, and temperature profile). The experimental research findings were obtained using a semi-operational injection moulding press where the injection is provided by a working screw. As the input raw material, wheat straw and spruce sawdust, HDPE plastic matrix and recycled HDPE, represented by lids from PET bottles, was used. The effect of the input raw material composition was determined according to a combination and default levels of biomass/HDPE concentration ratio, using recycled HDPE instead of virgin HDPE and particle size of biomass.

Elemental metal variance in cell culture raw materials for process risk profiling.

Elemental metals are critical raw material attributes which can impact cell culture performance and associated therapeutic protein product quality profiles. Metals such as copper and manganese act as cofactors and reagents for numerous metabolic pathways which govern cell growth, protein expression, and glycosylation, thus mandating elemental monitoring. The growing complexity of modern cell culture media formulations adds additional opportunities for elemental variance and its associated impact risks. This article describes an analytical technique applying inductively coupled plasma mass spectrometry to characterize a list of common raw materials and media powders used in mammalian cell culture and therapeutic protein production. We aim to describe a method qualification approach suitable for biopharmaceutical raw materials. Furthermore, we present detailed profiles of many common raw materials and discuss trends in raw material subtypes. Finally, a case study demonstrating the impact of an unexpected source of raw material variation is presented along with recommendations for raw material elemental risk profiling and control.

Contribución de las variables que estimulan la producción agraria en España (1990-2009)

The aim of this paper consists on analysing the evolution of the production of major agricultural commodities in Spain during the period 1990-2009. The main raw materials are defined by calculating the percentage of the following ratio: the value of production of each raw material to the value of total production of its agriculture sub-sector. Subsequently, the decreasing order of those values lets us consider the main raw materials as the first ones of that order for each sub-sector. Then we can examine, using multivariate statistical techniques, the relation between changes in the production of each major raw material and other variables such as the prices got by farmers, the CAP reforms and the changes in other relevant variables like the area harvested or the crop yield. The results show us that those which best explain the Value of Spanish Agricultural Production are the land variables and the price received.

Systematic sampling of quartzites in sourcing analysis: intra-outcrop variability at Naibor Soit, Tanzania (part I)

Despite the common exploitation of quartzites as raw materials during the African Stone Age, petrographic and geochemical characterization studies have been rarely undertaken. The Naibor Soit outcrop at Oldupai Gorge (Tanzania), considered the main source for quartzite procurement in the area, probably represents the exception to this analytical scarcity. Previous studies have determined variability among quartzites and the existence of singular mineral assemblages and geochemical compositions. In this study, we present the first systematic survey and EDXRF characterization of 86 samples from the two inselbergs that conform Naibor Soit. Samples are classified into nine macroscopic varieties and are mineralogically composed of quartz, muscovite, occasional hematite, rutile, magnetite, anhydrite, and fuchsite. Geochemical analyses, which include previously published data, confirm the existence of inter-/intra-outcrop variability and suggest compositional similarities among some areas of both inselbergs. Our study contributes compositional data and elevated predictive values for future archeological sourcing studies at Oldupai. Our results emphasize the necessity of systematic surveying and sampling to fully understand raw material variability and the factors that may have influenced their selection and exploitation.

Considerations on impact of raw material variability in gene therapy manufacturing: applying lessons learned from biologics manufacturing

Considerations on impact of raw material variability in gene therapy manufacturing: applying lessons learned from biologics manufacturing

Preparation process of bio-oil and bio-asphalt, their performance, and the application of bio-asphalt: A comprehensive review

The objective of this paper is to establish the state of knowledge on fast pyrolysis of bio-oil and bio-asphalt binder and to facilitate efforts in improving the overall performance of bio-asphalt and maximizing its road application. On the basis of reviewing the relevant literature recently, the fast pyrolysis (FasP) preparation process of bio-oil and its main properties, the preparation process of bio-asphalt and its performance and the application of bio-asphalt have been summarized. Due to the variations in raw materials, the adopted methods of FasP to prepare bio-oil could be different, and the properties of bio-oil from different sources are also different. At present, the plant-based bio-oil (mainly derived from wood waste and sawdust) has been widely used to prepare the bio-asphalt. Research on the low-temperature flexibility, high-temperature rheology, workability and other performance of biological asphalt showed that the workability and high-temperature performance of most asphalt are improved after adding bio-oil. However, the low-temperature performance is found to relatively reduce. Also, with regards to its application as a rejuvenator, bio-oil can considerably rejuvenate the aged asphalt's mixture performance. By far, most of research on bio-asphalt is still focused on the performance of bio-asphalt binder in the laboratory; its application in practical road engineering is still to be examined. This review also provides an outlook for the future, for example, establishing an integrated preparation process from bio-oil to bio-asphalt, and evaluating the properties of bio-asphalt by new standards. • The bio-oil products are mainly affected by the source of biomass, preparation temperature and catalyst. • It is feasible to replace asphalt partly with bio-oil as the binder due to the similar chemical composition. • The high-temperature performance and workability of the base asphalt improved after adding bio-oil. • The bio-asphalt can be used as road binder, bitumen modifier and rejuvenator.

Analisis faktor – faktor yang mempengaruhi produksi pada industri rumah tangga (studi kasus : industri kerupuk udang di Desa Lambur Luar Kecamatan Muara Sabak Timur Kabupaten Tanjung Jabung Timur)

The purpose of this study is (1) To analyze the socioeconomic characteristics of Producers in the Shrimp Crackers Home Industry in the Village of Lambur Luar Tanjung Jabung Timur Regency, (2) To analyze the effect of capital, labor, prices and raw materials on production in the Home Industry Shrimp Crackers in Lambur Luar Village, Tanjung Jabung Timur Regency. The results showed that all independent variables (capital, labor, prices and raw materials) affected the production of the shrimp cracker industry in Lambur Luar Village. Judging from the test results obtained by the coefficient of determination ( ) of 0.963460. This is aimed at capital, labor, prices and raw materials able to influence production by 96.34 percent and the remaining 3.66 percent for other variables not included in this study. Whereas if tested partially, capital, labor, price and raw material variables have a positive and significant effect on production in the shrimp cracker industry in Lambur Luar Village.

Micronization and Agglomeration: Understanding the Impact of API Particle Properties on Dissolution and Permeability Using Solid State and Biopharmaceutical “Toolbox”

The study aims to investigate the effect of agglomeration on micronized model active pharmaceutical ingredient (API) and to unravel the influence of API agglomerate strength on dissolution and permeation. Model API (micronized and unmicronized) were characterized using X-ray powder diffractometry (XRPD), differential scanning calorimetry (DSC), Raman imaging, optical profilometry, and miniaturized dissolution apparatuses. Two-way ANOVA with replication was used to interpret results with statistical significance. The API used in this study is most stable, non-solvated/non-hydrated, non-ionizable, and neutral in nature and belongs to BCS class III. The morphology of the API was needle or rod shaped, and to achieve desired manufacturability, API was micronized; however, the micronized API showed slower dissolution. Micronization resulted in high surface energy which led to the formation of agglomerates. The most significant conclusion is the integration, as well as implementation, of material science tetrahedron during early stages of formulation development (micronization) which would help to understand raw material variability. This would circumvent the undesired effects of API agglomeration and accelerate the transformation from R&D prototyping to commercialization.

Roadmap for Quality by Design Implementation for Dietary Supplements.

Recently, there is a growing interest in quality by design (QbD) in the dietary supplements industry. Effective QbD implementation necessitates a comprehensive understanding of multiple factors such as; the relationship between the critical qualities attributes (CQAs) and the scientific properties of the dietary supplement, the relationship between the manufacturing process, CQAs and the variability in raw materials. There are still two critical challenging concerns to be addressed in the implementation of QbD for herbal drugs. First, the quality variation of botanical raw materials and second, the difficulty in defining the satisfactory ranges of CQAs. In order to tackle these challenges, this review provides a snapshot of the numerous techniques that can be used in the identification and characterization of the botanical raw materials, therefore, will help in the successful QbD implementation for botanicals and dietary supplements. In order to implement this approach and to be considered in the current dietary supplement regulations, fruitful discussions and collaborations between academia, industry and regulatory agencies are required.

Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty

Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management.A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies.This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.

Lithium-Ion Battery Screening by K-Means with DBSCAN for Denoising

Batteries are often packed together to meet voltage and capability needs. However, due to variations in raw materials, different ages of equipment, and manual operation, there is inconsistency between batteries, which leads to reduced available capacity, variability of resistance, and premature failure. Therefore, it is crucial to pack similar batteries together. The conventional approach to screening batteries is based on their capacity, voltage and internal resistance, which disregards how batteries perform during manufacturing. In the battery discharge process, real time discharge voltage curves (DVCs) are collected as a set of unlabeled time series, which reflect how the battery voltage changes. However, few studies have focused on DVC based battery screening. In this paper, we provide an effective approach for battery screening. First, we apply interpolation on DVCs and give a method to transform them into slope sequences. Then, we use density-based spatial clustering of applications with noise (DBSCAN) for denoising and treat the remaining data as input to the K-means algorithm for screening. Finally, we provide the experimental results and give our evaluation. It is proved that our method is effective.

Engineering and Economic Evaluation of Production of SnO2 Nanoparticles by Microwave-Assisted Green Synthesis

The synthesis of nanoparticles from noble metals such as tin (IV) oxide (SnO2) is a research in progress with a very wide application in various fields, such as environmental improvement, gas sensors, catalysis, and lithium-ion batteries. The purpose of this study was to evaluate the economic feasibility of producing tin (IV) oxide (SnO2) nanoparticles using the microwave-assisted 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), Payback period (PBP), Cumulative Net Present Value (CNPV), as well as economic variations in sales, taxes, raw materials, labor wages, and utilities to ascertain project viability. Technical analysis to produce 8.54 kg of SnO2 nanoparticles per day shows a total production cost of 1,982,243,613.12 IDR and a total investment cost of 1,732,590,765.12 IDR. The resulting gross profit margin is 39,231,578,268 IDR/year, 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 SnO2 nanoparticles.

Monitoring of Nonlinear Processes With Multiple Operating Modes Through a Novel Gaussian Mixture Variational Autoencoder Model

Customized production, quality variation of raw materials and other factors make industrial processes work in multiple operating modes. In general, complex industrial processes have strong nonlinearity under each operating mode. In this paper, a Gaussian mixture variational autoencoder (GMVAE) model, which combines with Gaussian mixture and VAE, is proposed to monitor nonlinear processes with multiple operating modes. Due to the Gaussian mixture distribution limitation in latent variable space, GMVAE can not only automatically extract features of the nonlinear system, but also make these features follow Gaussian mixture distribution. Based on Gaussian mixture distribution in latent variable space and the reconstruction error, two probability monitoring indexes are constructed, whose control limits can be determined by χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution. TE benchmark data and real hot strip mill process (HSMP) data have been used to verify the effectiveness of the proposed method.

Automatic real‐time calibration, assessment, and maintenance of generic Raman models for online monitoring of cell culture processes

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real-time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in. Furthermore, once models are calibrated, it is typical for the model performance to degrade over time due to various recipe changes, raw material variability, and process drifts. Maintaining the performance of industrial Raman models is further complicated due to the lack of a systematic approach to assessing the performance of Raman models. In this article, we propose a real-time just-in-time learning (RT-JITL) framework for automatic calibration, assessment, and maintenance of industrial Raman models. Unlike traditional models, RT-JITL calibrates generic models that can be reliably deployed in cell culture experiments involving different modalities, cell lines, media compositions, and operating conditions. RT-JITL is a first fully integrated and fully autonomous platform offering a self-learning approach for calibrating and maintaining industrial Raman models. The efficacy of RT-JITL is demonstrated on experimental studies involving real-time predictions of various cell culture performance parameters, such as metabolite concentrations, viability, and viable cell density. RT-JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, assessed, and maintained, which to the best of authors' knowledge, have not been done before.

A Machine Vision Sensor for Quality Control of Green Anode Paste Material

A machine vision sensor was developed for predicting deviations from the optimum amount of pitch in anode formulations using paste texture analysis. It could help operators mitigate the impact of the increasing variability of anode raw materials (coke and pitch). Paste samples were formulated in the laboratory using dry aggregate mixes obtained using two cokes having different properties and various amounts of pitch. These were imaged, formed into small cylindrical anodes, and baked to measure their density. A combination of image texture methods was used for extracting relevant paste textural features. The latter were then used as inputs of partial least squares regression models to predict deviations from the maximum baked density. Good prediction results were obtained. Furthermore, the sensor was able to detect when the paste was at the optimal amount of pitch for both cokes and to measure deviations from it.