Raw Material Variability Research Articles

Carbohydrate composition of cow's milk and plant-based milk alternatives

The prevalence of lactose intolerance is one of the factors driving consumers toward plant-based milk alternatives (PBMA). This study aimed to analyze the carbohydrate profile of cows' milk (regular and lactose-free from both pasteurized and UHT milks; n = 80) and PBMA (n = 60) by HPLC. The study revealed that there was no significant difference in the energy content and total carbohydrate content between regular milk and lactose-free milk. Although milk and PBMA are entirely different food matrices, some PBMA types, as soya and coconut, may have energy contents and total carbohydrate contents comparable to those of milk. Furthermore, the variability observed in total carbohydrate content, as well as in carbohydrate profile, both between PBMA types and within samples of the same type, arises not only from variations in raw materials but also from the number of dilutions of the vegetable extract and the addition of different types and levels of carbohydrates, such as sucrose, fructose, or sorbitol, during PBMA manufacture. Although, milk presents a regular carbohydrate composition, differing solely between presentations (regular/lactose-free), the PBMA differs significantly between types and among the same type, not being for that reason regarded as a milk substitute.

Batch-to-Batch Differences in Nutritional Quality of Selected Raw and Cooked Pork Offal

With a pressing need to enhance sustainability and reduce waste, harnessing the untapped nutritional wealth buried in by-products of the meat processing industry is more critical than ever. The research was aimed at comparing variation in raw material between batches and establishing a comprehensive database that would serve as a valuable resource for manufacturers seeking to exploit the potential of pork offal. This study is focused on analysing both raw and cooked offal, including liver, heart, kidney, spleen, blood, tongue, esophagus, as well as meat trimmings. Four batches were analysed with regard to their chemical composition, micro- and macro-element content, amino as well as fatty acid profile. The results indicate significant differences in raw material supplies from the same supplier across batches. In our study, all analysed pork offal and meat trimmings exhibited statistically significant variations between individual batches. Furthermore, clustering analysis conducted on the data will be beneficial in optimising recipes and formulations that utilise pork offal. This research may contribute to the sustainable utilisation of meat by-products, waste reduction and overall value enhancement in the meat processing industry.

Proses Negosiasi Antara UMKM A.M Tahu Dan Pemasok Bahan Baku Produksi

SME’s are an industry that play an important role in the Indonesia’s economic development. A.M Tahu as one of SME’s have problems in fulfilling raw materials. The existence of special orders and moral hazard from suppliers were often obstacles in the production process so that A.M. Tahu is needed to make a negotiation process to get affordable prices, good quality, and flexibility in the quantity of raw materials with a limited choice of suppliers. The negotiation process certainly does not immediately reach the desired agreement, but efforts need to be made to convince both parties. The research aims to determine the negotiation process that occurs between A.M Tahu and raw material suppliers in reaching agreements. The types of data used in this study are primary and secondary data using AHP (Analysis Hierarchy Process) analysis methods and qualitative descriptive. The results showed that the negotiation process for A.M Tahu has several stages in reaching an agreement between the two parties, including starting from preparation, meetings, offers, and finally agreements. A.M Tahu gets a more convenient raw material price of Rp3,000 per kilo than the price should be, soybean quantity flexibility, and good quality assurance of soybean grain raw materials. A.M Tahu is also prioritized to choose suppliers who are outside the sub-district, and make raw material variables the main thing to consider in supplier selection.

Thermal Performance of Lightweight Earth: From Prediction to Optimization through Multiscale Modeling

This study investigates the prediction of the thermal conductivity of lightweight earth and raw earth blocks incorporating plant aggregates. Given the high variability of raw materials, it is not currently possible to predict the thermal performance of this type of material before sample production. This is a major obstacle to using these eco-materials, although their use is widely encouraged to improve building performance under evolving regulatory frameworks such as The French RE2020 standard. The incorporation of plant aggregates into earth-based materials offers improved insulation properties without compromising their mechanical integrity, positioning them as promising sustainable alternatives. Mean-field homogenization techniques, including the Mori-Tanaka as well as double inclusion models, are used to develop predictive tools for thermal behavior, using rigorously selected experimental data. The selected methods are particularly relevant. The Mori-Tanaka model appears to be better suited when the proportion of aggregates is limited, whereas the double inclusion scheme proves its worth when a higher proportion of aggregates is incorporated. This study emphasizes the influence of aggregate types and processing methods on thermal conductivity, highlighting the need for precise formulation and processing techniques to optimize performance. This paper demonstrates the relevance of the applied homogenization techniques applied. It enables the real morphology of the materials studied, such as aggregate shape and intrinsic cracking, to be taken into account. It contributes to the advancement of eco-material modeling toward predictive digital twins, with the goal of simulating and optimizing complex material behavior under various environmental conditions.

Recommendation on the selection of powdered activated carbon as carrier to enhance performance of polymeric UF membrane

The addition of powdered activated carbon (PAC) has been widely accepted as a solution to alleviate membrane fouling and maintain a sustainable flux in membrane bioreactor (MBR) and anaerobic membrane bioreactor (AnMBR). This is due to PAC's unique characteristics, such as serving as a supportive medium for bacterial attachment and growth, achieving adsorption of organic foulants and providing abrasion effect that minimizes the formation of cake layers on membrane surfaces. While majority of ultrafiltration (UF) membranes used in MBR and AnMBR application are polymeric, no study has yet investigated the integrity of polymeric UF membranes with the addition of PAC adsorbents. The abrasive effects of PAC on membrane surfaces under gas sparging in AnMBR or air scouring in MBR have not been studied, and no guidelines have been established for selecting a reliable PAC for such purposes. To address these concerns, this study systemically investigated the integrity of polymeric UF membranes with five different types of PACs to establish recommendations for PAC selection. Based on the integrity testing of polymeric UF membranes, a recommendation for PAC selection was formulated, highlighting bulk density and Gold Number (GN) as pivotal criteria. Five distinct types of PACs were studied to determine the optimal characteristics for polymeric UF membranes, considering variations in raw materials (coal-based and wood-based), activation methods (chemical and steam), particle morphology (sharp and granular), bulk densities (ranging from 0.23 to 0.6 kg/L) and GNs (spanning from 0.1 to > 6.0). The investigation extended beyond 100 d or until irreversible defects were observed in the UF membranes. This study provides guidelines for selecting PACs to be used with polymeric UF membranes, aiming to improve membrane performance while minimising their impact on membrane surfaces.

Impact of surfactant raw material variability on extrudate clarity appearance (transparency) in HME continuous manufacturing

The appearance of an extrudate formulation was monitored during hot-melt extrusion (HME) continuous manufacturing over 3 days. The formulation matrix consisted of a polymeric component, copovidone, and a low molecular weight surfactant, polysorbate 80. Based on studies prior to the continuous manufacturing, the desired appearance of the target extrudate is translucent. Although process parameters such as feed rate and screw speed were fixed during the continuous manufacturing, the extrudate appearance changed over time from turbid to translucent. For root-cause investigation, the extrudates were analyzed offline by differential scanning calorimetry (DSC) and advanced polymer chromatography (APC™). Although the polysorbate 80 content of both turbid and translucent extrudates was within target, the glass transition temperature of the turbid extrudate was 2 °C above expected value. The observed turbidity was traced to lot-to-lot variability of the polysorbate 80 used in the continuous manufacturing, where APC™ analysis revealed that the relative content of the low molecular weight component varied from 23% to 27% in correlation with the evolution from turbid to translucent extrudates. This work stresses the importance of taking feeding material variability into account during continuous manufacturing.

A FRAMEWORK FOR LEAN MANUFACTURING IMPLEMENTATION IN THE TEXTILE INDUSTRY: A RESEARCH STUDY

This study investigates the implementation of lean manufacturing in the textile industry, focusing on its potential to enhance efficiency, reduce waste, and improve product quality. Lean manufacturing principles, including Just-In-Time (JIT) production, Kanban systems, value stream mapping, 5S workplace organization, and continuous improvement (Kaizen), have been widely recognized for their effectiveness in various manufacturing sectors. However, their application in the textile industry remains underexplored. Through a systematic literature review of 60 review papers and empirical analysis involving case studies, interviews, and surveys with industry experts, managers, and workers, this research identifies the significant benefits of lean practices in textile manufacturing. The findings reveal substantial efficiency gains, waste reduction, and quality improvements among companies adopting lean principles. Nevertheless, the study also highlights several industry-specific challenges, such as high variability in raw materials, complex production processes, and market pressures from fast fashion. These challenges necessitate tailored approaches and effective change management strategies for successful lean integration. Additionally, the research emphasizes the need for investment in advanced technologies and flexible manufacturing systems to enhance responsiveness to market changes. The study concludes that despite the challenges, lean manufacturing offers valuable strategies for textile companies aiming to improve their competitiveness and sustainability in the global market. This research contributes to the growing body of knowledge on lean manufacturing and provides practical insights for textile manufacturers seeking to adopt and optimize lean practices.

Back-propagation ANN to Predict Cleanliness and Quality of Cotton Spinning Preparatory Output: A Comprehensive Research

Background: Modern spinning preparatory has undergone drastic technological changes, but still, individual’s expertise-based decisions govern the complex and non-linear multivariant relationships prevailing amongst raw material (cotton) variables, machine variables, process variables (waste), and product (card sliver) quality. The scientifically precise prediction regarding the cleanliness and quality of card sliver and waste control for the given inputted cotton variables processed on the state-of-the-art machinery setup without waiting for the production and testing of card sliver is still impossible. Methods: The present work describes the use of Aritificial Neural Networks (ANN) for ruling out these limitations on scientific grounds. A complex system targeted at ANN was developed using the "newff" function on the mill's five-year database. Single-group ANN was initially designed to determine the influence of inherent variations in raw cotton fibre properties and trash content on blow room and card performance. A multi-group approach of ANN was developed at a later stage to define the influence of complex interactions amongst various fibre properties on three main quality measures considered at blow room and carding, viz., i) influence of blow room and card on fibre length properties, ii) fibre damage at blow room or improvement at card, and iii) degree of cleanliness of the output material. Results: Reverse modelling for both groups was also successfully designed to demarcate feed cotton quality and cleanliness requirements for targeted blow room or card cleaning performance. Conclusion: A high level of positive endurance was observed for all ANNs. Multigroup networking has proven to be more precise than single group networking.

Cycle-Based Control of Injection Moulding Process in Presence of Material Dual Sourcing Using Mass Feedback.

The low cost and precise tolerances of plastic injection moulded products are a major reason for the popularity of the manufacturing method. The tolerances are greatly influenced by the equipment, raw material and moulding process. One challenge is the raw material variation. This paper presents a production process using cycle based feedback of cycle mass, for control of part properties in the presence of material variation from dual sourcing. The part properties considered are part mass and outer dimensions. The process uses direct cycle mass feedback without additional process measurements in the proposed controller structure. The designed controller structure is tested in a multi-cavity mould while using raw materials from multiple vendors, encompassing five different grades. The results show a total decrease in part mass variance of approximately 50% and a decrease of length and width variance of approximately 40% compared to a moulding process with fixed settings.

Geopolymer Cement in Pavement Applications: Bridging Sustainability and Performance

Sustainability and the quest for a more robust construction material cannot be divorced from each other. While Portland cement has revolutionized the construction sector, its environmental toll, particularly in greenhouse gas emissions and global warming, cannot be ignored. Addressing this dilemma requires embracing alternatives like geopolymer cement/geopolymer binder (GPC/GPB). Over the last few decades, considerable strides have been achieved in advancing GPC as a sustainable construction material, including its utilization in pavement construction. Despite these advances, gaps still exist in GPC optimal potential in pavement construction, as most studies have concentrated on specific attributes rather than on a comprehensive evaluation. To bridge this gap, this review adopts a novel, holistic approach by integrating environmental impacts with performance metrics. To set the stage, this review first delves into the geopolymer concept from a chemistry perspective, providing an essential broad overview for exploring GPC’s innovations and implications in pavement applications. The findings reveal that GPC not only significantly reduces greenhouse gas emissions and energy consumption compared to Portland cement but also enhances pavement performance. Further, GPC concrete pavement exhibits superior mechanical, durability, and thermal properties to ensure its long-term performance in pavement applications. However, challenges to GPC utilization as a pavement material include the variability of raw materials, the need for suitable hardeners, the lack of standardized codes and procedures, cost competitiveness, and limited field data. Despite these challenges, the process of geopolymerization presents GPC as a sustainable material for pavement construction, aligning with Sustainable Development Goals (SDGs) 3, 9, 11, and 12.

Past human decision-making based on stone tool performance: Experiments to test the influence of raw material variability and edge angle design on tool function

One of the main interests in the interpretation of the archaeological record and its variability within and through time and space is the production and use of past human stone tool technologies. Tool design and function are inevitably intertwined and strongly related to tool use. Understanding tool design provides information about early human technological adaptations and reflects human behaviour in the sense of conscious or unconscious decision-making. Nevertheless, the reason for major changes (including novelties, innovations, and loss) in past human stone tool technology is still poorly understood. A comprehensive approach focusing on tool function (What was the tool meant for?) and use (What was the tool used for?) can help to overcome this gap. While tool function (including performance) can be investigated experimentally, tool use can be addressed with use-wear analyses. These questions can be best investigated on technological systems showing little tool variability but strong evidence of maintenance and long-term use, such as Middle Palaeolithic industries.The Late Middle Palaeolithic record of Central and Eastern Europe is marked by the emergence of an asymmetric tool-type called Keilmesser (bifacial backed knives). Due to their sophisticated morphology, Keilmesser as a case study offer the potential to address aspects of raw material selection, tool production, maintenance, and reworking.This paper presents the results of an experiment designed to study the tool performance of Keilmesser from three archaeological sites, namely Balver Höhle, the Upper site of Buhlen and Grotte de Ramioul by testing raw material, edge angle and movement as independent variables. A highly controlled, sequential experiment was conducted using a mechanical device performing unidirectional cutting and carving movements on hard contact material. Results demonstrate the possibility to perform the mentioned task with 35° and 45° edge angles, maintaining function, albeit at differing levels of efficiency. The data has a direct impact on the interpretation of the archaeological assemblages regarding aspects such as stone tool morphology and resharpening. At the same time, the study highlights the importance of raw material analysis to understand the variability in the archaeological record and the implications on past human decision-making strategies.

Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

Uncertainty analysis for the dynamic modulus of recycled asphalt mixtures using unclassified fractionated RAP materials

The use of unclassified reclaimed asphalt pavement (RAP) is limited due to concerns about raw material variability, impacting RAP mixture performance consistency. Space constraints lead to aggregating recycled materials from various projects into "unclassified RAP," hindering its potential benefits. The evaluation of uncertainties, particularly in dynamic modulus |E*|, is crucial for assessing performance and reliability in asphalt mixtures containing unclassified RAP. In this research, A systematic approach for calculating the coarse and fine RAP ratios based on unclassified RAP content, along with regulated processing and fractionation meeting standards was implemented. Then, a probabilistic model was established, utilizing the sigmoidal function, to analyze the uncertainty in |E*| as a function of the reduced frequency for asphalt mixtures with varying unclassified RAP contents (0%, 15%, 25%, and 45%). The findings revealed that |E*| uncertainty was minimal at low temperatures, reasonable at service temperatures, and critical (COV > 30%) at high temperatures for standard and RAP mixtures. Importantly, increasing unclassified RAP content doesn't worsen |E*| uncertainty. Mixes with high unclassified RAP content showed similar or lower COV values across analyzed frequencies. The proposed approach can reduce COV values in high unclassified RAP content mixes, offering a practical solution to leverage these materials' benefits.

Bacterially enhanced plant-growing media for controlled environment agriculture.

Microbe-plant interactions in the root zone not only shape crop performance in soil but also in hydroponic cultivation systems. The biological and physicochemical properties of the plant-growing medium determine the root-associated microbial community and influence bacterial inoculation effectiveness, which affects plant growth. This study investigated the combined impact of plant-growing media composition and bacterial community inoculation on the root-associated bacterial community of hydroponically grown lettuce (Lactuca sativa L.). Ten plant-growing media were composed of varying raw materials, including black peat, white peat, coir pith, wood fibre, composted bark, green waste compost, perlite and sand. In addition, five different bacterial community inocula (BCI S1-5) were collected from the roots of lettuce obtained at different farms. After inoculation and cultivation inside a vertical farm, lettuce root-associated bacterial community structures, diversity and compositions were determined by evaluating 16S rRNA gene sequences. The study revealed distinct bacterial community structures among experimental replicates, highlighting the influence of raw material variations on root-associated bacterial communities, even at the batch level. However, bacterial community inoculation allowed modulation of the root-associated bacterial communities independently from the plant-growing medium composition. Bacterial diversity was identified as a key determinant of plant growth performance with green waste compost introducing Bacilli and Actinobacteria, and bacterial community inoculum S3 introducing Pseudomonas, which positively correlated with plant growth. These findings challenge the prevailing notion of hydroponic cultivation systems as sterile environments and highlight the significance of proper plant-growing media raw material selection and bacterial community inoculation in shaping root-associated microbiomes that provide stability through microbial diversity. This study supports the concept of creating bacterially enhanced plant-growing media to promote plant growth in controlled environment agriculture.

Improved quality control and sustainability in food production by machine learning

In recent years, the food industry has faced a number of complex challenges related to both quality control and sustainability. Ensuring consumer safety and satisfaction remains a cornerstone of the food industry, supported by stringent standards that address the risks of contamination and spoilage. However, variability in raw materials, processing techniques and storage conditions are just some of the factors that affect quality in the food industry. To manage this high variability, it is essential to analyse the production process and factors that most influence food quality, aiming to predict and minimise food waste, thereby ensuring a sustainable process. This convergence of quality control and sustainability goals provides fertile ground for machine learning applications. By improving defect detection, process optimisation, resource allocation and predictive maintenance, these models help to improve product quality and reduce environmental impact. This article aims to explore the various applications of machine learning models in the food industry, where the variability of raw materials and the difficulty of controlling production and environmental factors challenge the use of traditional methods. The quality control and sustainability of an industrial corn cakes production process is used as a case study.

Review of Solid Waste Resource Utilization for Brick-making

The utilization of solid waste resources is an essential strategy in the pursuit of sustainable development. Among the various techniques used for solid waste treatment, brick-making technology stands out as a key approach to facilitate the utilization of solid waste resources. This article provides a comprehensive review and summary of the recent research progress in the field of solid waste resource utilization for brick-making. It mainly focuses on the current state and technological advancements in brick-making using solid waste. Considering the influence of different external factors and variations in brick-making raw materials, this study employs the Citespace software to conduct co-word and clustering analysis, with “resource utilization” as the primary keyword. Furthermore, it introduces the brick-making technology utilizing sludge and red mud as raw materials, while also elaborating on advanced techniques and practical experiences in utilizing industrial and urban solid waste as raw materials. Additionally, the article presents an overview of the key development trends in this field and puts forward several recommendations for future research to aid designers in furthering their investigation.

Rotary Kiln Temperature Control Under Multiple Operating Conditions: an Error-Triggered Adaptive Model Predictive Control Solution

The temperature of rotary kiln, as one of the essential equipment in zinc smelting process, determines the product quality and resource utilization. However, since rotary kiln is a large-scale and highly coupled system, there are plenty of variables affecting the rotary kiln temperature, bringing too much redundant information to describe rotary kiln dynamics. In addition, due to the variations in raw materials, production load, and market demand, there exist various operation conditions, making it difficult to achieve stability control of the rotary kiln temperature with traditional control methods. To solve these problems, an error-triggered adaptive model predictive control (ET-AMPC) is proposed in this article. Specifically, since rotary kiln temperature is hard to regulate due to redundancy among variables and strong nonlinearity, an orthogonal maximum mutual information coefficient feature selection (OMICFS) method is first proposed to determine vital variables affecting temperature most. Then, aiming at the problem of changing operating conditions of rotary kilns, an ET-AMPC method is proposed, which can precisely adapt to different operating conditions and achieve stability control. Finally, experiments on a numerical simulation case and an industrial rotary kiln show the strength and reliability of the proposed method, which reduces 10%–20% trajectory tracking error in the period of operating condition changing and improves the control accuracy effectively.

Analisis Biaya Bahan Baku Dalam Meningkatkan Volume Produksi

The purpose of this study was to determine and analyze the relationship between the cost of raw material variables in increasing production volume. The research method used is a qualitative method and the data used is secondary data obtained directly through interviews with company owners. In this study, sampling used a periodic data approach on a monthly scale, namely 36 months. What the writer examines consists of one independent variable and one dependent variable, so the most appropriate analysis tool is simple linear regression. Based on the results of simple linear regression analysis, it is known that the cost of raw material has a significant effect on production volume. This can also be seen from company data that each increase in the cost of raw material, the production volume also increases. The calculation results show that there is a very close relationship between the variable costs of raw material in increasing production volume.

Adjusting plant operating conditions to widen multivariate specification regions for incoming raw materials – An optimization framework

Decision tools to determine the acceptability of an incoming lot of raw materials prior to its purchase from a supplier is essential to ensure efficient quality control. They allow mitigating the propagation of the raw material variability through the process and, ultimately, in the final product quality. Establishing multivariate specifications regions for raw material properties to achieve high and consistent end product quality using latent variable models is key. However, these specifications are typically designed assuming the process operates at nominal conditions. The objective of this work is to enlarge the raw material acceptance region to accept materials having a wider range of properties, and possibly at a lower cost, while meeting final product quality requirements. Using a sequential multi-block partial least squares model (SMB-PLS), the design of multivariate specification regions is combined with optimization of process operating conditions to better cope with fluctuations in raw material properties. An economic criterion is used to assess whether accepting lower cost materials, at the expense of adjusting plant operation, which incurs additional costs, is economically justified. The approach is illustrated using a grinding-flotation plant simulator where the objective is to assess the profitability of different lots of raw materials (i.e. ores). The impact of measurement noise is also investigated. The case studies show that the raw material processability is improved since the number of correctly accepted lots is increased by 7.3 % in comparison with maintaining nominal process conditions.

Effect of raw material structural composition on the fermentation process of ethanol production

Ethanol is becoming the important renewable energy sources in the world which produces from carbohydrate resources. The raw material selection is the important procedure for real industrial production. The effect of biomass structural composition in feedstock on ethanol production is therefore needed to explore for feedstock selection. According to the previous research studies, the fermentation process includes pre-treatment and fermentation of sugar to ethanol. This study focuses on the simulation of raw material variation in ethanol production especially the structural composition of carbohydrate that contains three main components, cellulose, xylan, and lignin, in the fermentation section. The simulation model is validated by the data of previous study information with small deviation. The mixture design method is used for result interpretation and analysis. From the design of experiment, there are 14 scenarios and the selected response parameter is the mass fraction of ethanol in outlet stream of fermentation section. From the results, the maximum mass fraction of ethanol in outlet stream is in the scenario which has a large amount of cellulose and xylan fractions. This is because the conversion of reaction in the fermentation reactor mostly consumes cellulose and xylan to produce glucose and xylose, respectively, and converts them into ethanol product. From the overall result of this study, the important of raw material selection in ethanol production is illustrated.