Technical, Environmental, and Economic Analysis Comparing Low-Carbon Industrial Process Heat Options in U.S. Poly(vinyl chloride) and Ethylene Manufacturing Facilities.

Electrification and clean hydrogen are promising low-carbon options for decarbonizing industrial process heat, which is an essential target for reducing sector-wide emissions. However, industrial processes with heat demand vary significantly across industries in terms of temperature requirements, capacities, and equipment, making it challenging to determine applications for low-carbon technologies that are technically and economically feasible. In this analysis, we develop a framework for evaluating life cycle emissions, water use, and cost impacts of electric and clean hydrogen process heat technologies and apply it in several case studies for plastics and petrochemical manufacturing industries in the United States. Our results show that industrial heat pumps could reduce emissions by 12-17% in a typical poly(vinyl chloride) (PVC) facility in certain locations currently, compared to conventional natural gas combustion, and that other electric technologies in PVC and ethylene production could reduce emissions by nearly 90% with a sufficiently decarbonized electric grid. Life cycle water use increases significantly in all low-carbon technology cases. The levelized cost of heat of viable low-carbon technologies ranges from 15 to 100% higher than conventional heating systems, primarily due to energy costs. We discuss results in the context of relevant policies that could be useful to manufacturing facilities and policymakers for aiding the transition to low-carbon process heat technologies.

A novel process for the complete recycling of exhausted coffee capsules with a fully circular approach: Design of the industrial plant and Techno-Economic analysis of the process.

The objective of this paper is to present the technical and feasibility analysis of an innovative mechanical recycling system for exhausted coffee capsules. This recycling process involved the sorting of spent coffee grounds (SGC) and the subsequent drying and melt-mixing of a portion of these grounds with the remaining capsule components, mainly composed of polypropylene (PP), along with optional virgin PP. These newly developed composite materials exhibited mechanical and rheological properties comparable or even surpassing those of virgin PP. They were also successfully utilized for the injection of new capsule shells, thus alignin with the principles of the circular economy. In addition to the technical aspects, this paper present a comprehensive Techno-Economic Analysis (TEA) of the proposed recycling processes, considering the inclusion of virgin PP (0-20 %) and the initial moisture content (MSGC) of SGC(5-55 %) as varying factors. An industrial plant, designed to handle up to 190 million exhausted coffee capsules and produce up to 1500 tons of recycled compund was appropriately sized. The analysis revealed that processes are profitable across all examined scenarios and that the Net Present Value ranged between 800 k€ (for vPP = 20 % and MSGC = 55 %) and 2000 k€ (for vPP = 0 % and MSGC = 5 %).

Quantitative microbial spoilage risk assessment caused by fungi in sports drinks through multilevel modelling.

The risk of fungal spoilage of sports drinks produced in the beverage industry was assessed using quantitative microbial spoilage risk assessment (QMSRA). The most relevant pathway was the contamination of the bottles during packaging by mould spores in the air. Mould spores' concentration was estimated by longitudinal sampling for 6 years (936 samples) in different production areas and seasons. This data was analysed using a multilevel model that separates the natural variability in spore concentration (as a function of sampling year, season, and area) and the uncertainty of the sampling method. Then, the expected fungal contamination per bottle was estimated by Monte Carlo simulation, considering their settling velocity and the time and exposure area. The product's shelf life was estimated through the inoculation of bottles with mould spores, following the determination of the probability of visual spoilage as a function of storage time at 20 and 30 °C using logistic regression. The Monte Carlo model estimated low expected spore contamination in the product (1.7 × 10-6 CFU/bottle). Nonetheless, the risk of spoilage is still relevant due to the large production volume and because, as observed experimentally, even a single spore has a high spoilage potential. The applicability of the QMSRA during daily production was made possible through the simplification of the model under the hypothesis that no bottle will be contaminated by more than one spore. This simplification allows the calculation of a two-dimensional performance objective that combines the spore concentration in the air and the exposure time, defining "acceptable combinations" according to an acceptable level of spoilage (ALOS; the proportion of spoiled bottles). The implementation of the model at the operational level was done through the representation of the simplified model as a two-dimensional diagram that defines acceptable and unacceptable areas. The innovative methodology employed here for defining and simplifying QMSRA models can be a blueprint for future studies aiming to quantify the risk of spoilage of other beverages with a similar scope.

Application of machine learning for risky sexual behavior interventions among factory workers in China.

Introduction: Assessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models. Methods: The survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12. Results: About a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%). Discussion: Machine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services.

Tackling the Lead Gremlins: A Response to Take-Home Lead Exposure in a Minnesota Industrial Facility, 2019.

Lead exposure that occurs from contamination inadvertently brought home from a workplace is known as take-home exposure. Take-home exposures are a public health hazard that adversely affects health equity for families and communities. This article describes coordinated action by agencies in Minnesota to curb lead exposure among children of workers at a facility that produces fishing sinkers and battery terminals. (Am J Public Health. 2022;112(S7):S655-S657. https://doi.org/10.2105/AJPH.2022.306982).

ASSESSMENT OF OCCUPATIONAL EXPOSURE LEVELS OF CLEANING PRODUCT MANUFACTURING FACTORY WORKERS TO ELECTROMAGNETIC FIELDS.

Determination of the levels of the electromagnetic field of frequency between 0 Hz and 300 GHz bands in the working area and the evaluation of the results measured in terms of international standards is now essential according to the regulation of working safety and health. In this study, electromagnetic field measurements were performed in a cleaning product manufacturing factory with 400 workers. Electric and magnetic field measurements were performed at 10 different departments of the factory with extremely low-frequency and broadband (radiofrequency) frequencies. Obtained values are shown and analysed with graphs and compared by the International Commission on Non-Ionizing Radiation Protection and National Information Technology and Communication Authority (Turkey) limits. Also, an assessment is made for the occupational electromagnetic field levels in the factory and recommendations are suggested for the lowest risk occurrence and providing a healthier working environment for workers.

[Environmental Monitoring in a Pharmaceutical Manufacturing Facility Using a Culture Independent Approach].

Strict microbial control is required in manufacturing facilities to ensure the quality of pharmaceuticals and foods. Environmental microbial monitoring plays a fundamental role in reducing the risk of microbial contamination. Appropriate microbial control requires an understanding of abundance and community structures of microbes in the target environment. However, most of these microbes are not culturable using conventional methods. In this study, we determined the number of microbial particles and assessed the environmental microbiome in a pharmaceutical manufacturing facility, using high-throughput sequencing of rRNA gene fragments. Our results provide fundamental data for the evaluation and control of microbes in the pharmaceutical and food industries.

Qualitative and quantitative assessment of waste generation in a refrigerator-manufacturing plant based on a waste tree and mass balance.

In this study, wastes originating at each production station during refrigerator manufacturing were identified and classified based on a waste tree. A mass balance study revealed a total waste production factor of 0.046 kg/kg of a product of which 75.3%, 23.9%, and 0.8% were non-hazardous wastes (NHWs), packaging wastes (PWs), and hazardous wastes (HWs), respectively. Wastes produced during refrigerator manufacturing were grouped under 35 different waste codes. Waste codes that contributed more than 5% by weight were 15 02 02 (contaminated absorbent material), 15 01 10 (contaminated packaging), 16 02 13 (electronic cards), 07 02 14 (polyol) and 08 05 01 (isocyanates), 19 08 13 (treatment sludge), 16 02 15 (capacitors), and 13 01 13 (hydraulic oil) for HWs, 12 01 01 (ferrous metal), and 16 02 16 (components) for NHWs, and, finally, 15 01 03 (wooden), 15 01 01 (paper&cardboard), and 15 01 02 (plastic) for PWs over 5 years. Scrap costs were used as a surrogate to determine production stages that generated high amounts of metal and plastic wastes. Logarithmically, increasing and decreasing trends were observed for PWs and NHWs over the study period, respectively. HW amounts did not exhibit a statistically significant trend. Twenty-eight BATs (best available techniques) were identified that could be applied in refrigerator manufacturing for waste minimization and management. Among those, 8 of them were proposed for further improvement for waste management in the facility.

Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

RD internationalization, domestic technology alliance, and innovation in emerging market.

Although R&D internationalization plays an important role in enterprises' globalization, few studies explore the mechanism of R&D internationalization and emerging market companies' innovation, or the relationship between R&D internationalization, domestic technology alliances and absorptive capacity. How does the R&D internationalization of emerging market enterprises affect the innovation of those enterprises? Under fierce market competition, do absorption capacity and domestic technology alliances have a significant impact on enterprise innovation? From the perspective of the knowledge-based view, this paper studies 185 enterprises undergoing R&D internationalization in China from 2012 to 2017, using high-dimensional Poisson fixed effects model, we use instrumental (HDFE IV) estimation to explain the impact of R&D internationalization on the innovation of the parent company and the mechanism behind it. The study finds that R&D internationalization positively promotes the parent company's innovation, and domestic technology alliances and absorptive capacity play a partial mediator role in R&D internationalization. In the face of fierce market competition, domestic technical alliances play a significant role in promoting enterprise innovation, while absorptive capacity plays a negative role in promoting enterprise innovation with the moderating effect of market competition.

Biorreactores de un solo uso en la industria biofarmacéutica y su uso en la producción de candidatos vacunales contra SARS- COV-2. Artículo de revisión / Single-use systems bioreactors in the biopharmaceutical industry and its use in SARS-CoV-2 vaccine's candidates production. A review

Los biorreactores de sistemas de un solo uso (SUSs), también conocidos como biorreactores desechables, se han convertido en una parte integral de las instalaciones biotecnológicas de fabricación para bioproductos con un mercado potencial que espera una tasa de crecimiento de casi el 15,5% durante el período pronosticado: 2018 a 2023. Los biorreactores SUSs son más seguros, simples y flexibles al compararlos con sus contrapartes, biorreactores de acero inoxidable, por lo que su uso se está incrementando en la industria biofarmacéutica principalmente en la planificación de vías rápidas de proyectos complejos, incluidos los relacionados con la pandemia de SARS-CoV-2. Así, el uso de SUS se ha convertido en una alternativa eficaz para la producción rápida de candidatos a vacunas. Pero algunas desventajas técnicas y operativas aún obstaculizan su uso en todo el mundo. Esta revisión brinda una visión racional del uso, los tipos, los parámetros operativos y las nuevas aplicaciones de los biorreactores SUSs en la industria biofarmacéutica. Asimismo, también se discuten los parámetros apropiados y las limitaciones de este equipo, enfocándose en su uso para la producción de vacunas contra COVID-19

Single-Use-Systems (SUSs) Bioreactors, also known as disposable bioreactors, have become an integral part of biotechnology manufacturing facilities for bioproducts with a potential market expecting a growth rate of nearly 15.5% over the forecast period: 2018 to 2023. SUSs bioreactors are comparatively safe, simple, and flexible than their stainless-steel bioreactors counterparts thus, their usage is being augmented in the biopharmaceutical industry mainly in planning fast tracks of complex projects, including those related to the SARS-CoV-2 pandemic. Thus, the use of SUSs has become an effective alternative for the rapid production of vaccine candidates. However, some technical and operational disadvantages still hamper their worldwide use. This review gives a rational insight into SUSs bioreactors use, types, operational parameters and new applications in the biopharmaceutical industry. Likewise, the appropriate parameters and limitations of this equipment, focusing on its use for vaccine production against COVID-19 are also discussed

Relationship between occupational exposure to whole-body vibration and noise with sex hormone levels: An empirical assessment in an automobile parts manufacturing plant.

The present study investigated the relationship between occupational exposure to noise and whole-body vibration (WBV) with the levels of sex hormones in an automobile parts manufacturing plant. The level of workers' exposure (n = 162) to each of the mentioned stressors was measured through standard methods, and the time-weighted average of exposure was calculated for each person. In order to determine serum sex hormones (free testosterone, luteinizing hormone, and follicle-stimulating hormone), blood samples were taken from all participants after 8-10 h of fasting between 7 and 9 am and then the blood samples were analyzed by an enzyme-linked immunosorbent assay method. In general, regarding testosterone as the main male sex hormone, only 49% of the participants were in the normal range. In a total of three sections, the lowest mean testosterone levels were observed in the third exposure group (WBV > 1.93 m/s2; noise >92.69 dB) of the studied stressor; however, only the difference in testosterone levels between the three different groups of exposure to noise was statistically significant (p = 0.001). The relationship between demographic variables and levels of noise and WBV exposure with sex hormones was not linear and only the relationship between noise exposure and testosterone levels was statistically significant (R = -0.201, p = 0.013). According to the results of logistic regression, the WBV had the greatest effect on testosterone levels. However, according to the results of the correlation test, only the relationship between noise exposure and testosterone levels was statistically significant.

An Implementation Research Approach to Re-orient Health Supply Chains Toward an Equity Agenda in the COVID-19 Era.

The Covid-19 pandemic has exposed critical inequities in global healthcare supply chains and the need for these systems to be analyzed and reoriented with an equity lens. Implementation research methodology can guide the use of evidence-based interventions to re-orient health supply chains towards equity and optimize health outcomes. Using this approach, private and public sector entities can adapt their strategies to focus not just on efficiency and cost savings but ensuring that vulnerable populations have access to essential medications, vaccines, and supplies. Findings can inform regulations that address supply chain inequities at the global level, strengthen existing systems to fill structural gaps at the national level, and address contextual challenges at the subnational level. This methodology can help account for historical practices from prior health initiatives, identify contemporary barriers and facilitators for positive change, and have applicability to the Covid-19 pandemic and ongoing vaccine distribution efforts. An implementation research approach is critical in equipping health supply chains with a path for more resilient and equitable distribution of necessary supplies, vaccines, and delivery of care.