Production Scenarios Research Articles

Well-to-wheel carbon footprint and cost analysis of gasoline, diesel, hydrogen ICE, hybrid and fully electric city buses

Carbon emissions in mobility, as one of the primary causes of global warming, are regulated by various organizations with different legislations. Different energy sources and power-train solutions are used to comply with these constraints. Hydrogen-fueled, fully electric, and hybrid vehicles are at the forefront of these technologies. Within the scope of this study, gasoline-, diesel-, and hydrogen-fueled internal combustion engines, and fully electric and serial hybrid 18-m city bus vehicles were modeled. Comprehensive analyses of emissions and costs, considering both the entire energy production and consumption cycles for various vehicle types were conducted. Models that are primarily based on fuel consumption data show that while fully electric vehicles currently have the lowest emissions, hydrogen-fueled vehicles can play a more prominent role soon with the increasing production of green hydrogen. For the first step of hydrogen implementation in the industry, H2ICE is a promising for both reduced carbon emissions in mobility and cost-effectiveness. If the forecasted hydrogen production and distribution scenario is adopted by industrial and energy organizations, it is evident that by the year 2030, more environmentally friendly operation can be achieved particularly in the context of public transportation. Embracing this approach is likely to lead to positive environmental sustainability impacts in future transportation systems.

A Bayesian analysis of adaptation of mountain grassland production to global change

Abstract In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: While an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified. We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2085. We built a Bayesian Belief Network (BBN) from long‐term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios. Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the conservation LULC scenario (−28% on average between 2020 and 2085), followed by the tourism development scenario (−7%) and the business‐as‐usual scenario (+3%). Climate change under representative concentration pathways (RCP) 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Synthesis and applications: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannual variability threaten the current grass‐based protected designation of origin (PDO) production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. Along with decreased total annual production due to decreasing grassland area compounded by more extreme climate change, the seasonal shift in production and increased interannual variability threaten the current grass‐based PDO production system. Further Bayesian modelling co‐developed with local stakeholders and experts could greatly contribute to adaptation planning of the regional production system.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Análise da Produção Científica em Oncológicos na Base de Dados Scopus

According to data from the United Nations, cancer is the second leading cause of death in the world. Due to its chemical properties, technetium-99m remains the most used radioisotope in the diagnosis of cancers. Thus, the present work analyzed through bibliometrics the characteristics of the world scientific production on technetium-99m, presented in articles from the Scopus database between 2001 and 2021. The search terms “(99mtc OR technetium OR technetium-99m) were used. AND (cancer OR tumor)”, with the filters “Title” and “Summary” in the search fields united by the Boolean operator “OR”. Then, the data were exported so that the construction and visualization of bibliographic networks could be carried out using the VOSviewer® program. The analysis of articles and journals showed that, in the number of publications, the United States, the Journal of Nuclear Medicine, The Netherlands Cancer Institute and the author Mishra, A.K. (India). Regarding the analyzes analyzed with VOSviewer®, the following stand out: the author Shuang Liu (co-citation of authors); and the words article and human (co-occurrence of keywords); Fan Wang (bibliographic coupling). It is hoped that this study will help stakeholders who have carried out research on technetium-99m radiopharmaceuticals to understand the scenario of scientific production.

Metabolic Engineering of Corynebacterium glutamicum for the Production of Flavonoids and Stilbenoids.

Flavonoids and stilbenoids, crucial secondary metabolites abundant in plants and fungi, display diverse biological and pharmaceutical activities, including potent antioxidant, anti-inflammatory, and antimicrobial effects. However, conventional production methods, such as chemical synthesis and plant extraction, face challenges in sustainability and yield. Hence, there is a notable shift towards biological production using microorganisms like Escherichia coli and yeast. Yet, the drawbacks of using E. coli and yeast as hosts for these compounds persist. For instance, yeast's complex glycosylation profile can lead to intricate protein production scenarios, including hyperglycosylation issues. Consequently, Corynebacterium glutamicum emerges as a promising alternative, given its adaptability and recent advances in metabolic engineering. Although extensively used in biotechnological applications, the potential production of flavonoid and stilbenoid in engineered C. glutamicum remains largely untapped compared to E. coli. This review explores the potential of metabolic engineering in C. glutamicum for biosynthesis, highlighting its versatility as a cell factory and assessing optimization strategies for these pathways. Additionally, various metabolic engineering methods, including genomic editing and biosensors, and cofactor regeneration are evaluated, with a focus on C. glutamicum. Through comprehensive discussion, the review offers insights into future perspectives in production, aiding researchers and industry professionals in the field.

Privacy-preserving pathological data sharing among multiple remote parties

The sharing of pathological data is highly important in various applications, such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data poses significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.

PRODUCCIÓN DE JAMAICA (Hibiscus sabdariffa L.) EN MÉXICO: RETOS Y PROSPECTIVA ESTRATÉGICA

<p><strong>Background.</strong> Mexico has favorable agroclimatic conditions for the development of roselle, a crop with high world demand; however, it has low yields and domestic consumption relies more than 50% on imports. <strong>Objective. </strong>To describe the national production scenario and identifying the main challenges, as well as the strategic factors to increase potential of this crop in Mexico. <strong>Methodology.</strong> The research was conducted in three phases, using a descriptive, correlational and explanatory approach. Qualitative and quantitative information was collected through documentary review, in-depth interviews and panel of experts. The methods of analysis were descriptive statistics, correlational statistics and structural analysis MICMAC (Matrice d' Impacts Croisés Multiplication Appliqués à un Classement). <strong>Results.</strong> It was found that, in 2021, the per capita consumption was 137.58 g. During the period 2009-2019, the national production of roselle showed an increase of 56.2%, which is attributed to intensive growth. The high demand for labor in the harvest, the low prices of imported roselle, and the lack of government and scientific attention, impossibility of development of this crop. <strong>Implications.</strong> This study helps to understand the productive dynamics of roselle cultivation in Mexico as well as provides information and strategies to enhance its development. <strong>Conclusion.</strong> The key factors for the evolution of roselle production in the long term are differentiated public policies, competitiveness (substitutes, quality, prices) and social organization.<strong></strong></p>

PSII-10 Development of an interdisciplinary undergraduate and graduate course in precision livestock farming systems

Abstract The modern livestock industry is increasingly adopting data, technologies, and automation into daily husbandry and management routines and decisions. These advances in precision livestock farming (PLF) enable improved financial margins, sustainability, efficiency, and animal productivity and welfare. However, there is currently a gap between the engineers and data scientists designing the technologies and the animal scientists and caretakers using them. At North Carolina State University a novel course on PLF Systems for undergraduate and graduate students was developed in the Department of Animal Science. Through lecture delivery, livestock production scenarios, scientific literature, and data-based homework assignments students explored data collection, management, analysis, and data-based decision making. Students gained hands-on experience with a variety of sensor types and signal processing through lab-based activities. Basic hardware and software development concepts were explored during an Arduino-based microcontroller activity. The course was developed and refined using student feedback to include additional sensor and lab-based days and additional in-class supported activities utilizing Excel for data manipulation, processing, and presentation. Self-reported student learning results were collected across two semesters (19 students; 18 Animal Science, 1 Biological Engineering) with84% of students indicated the course was effective in increasing their ability to evaluate and implement technology in livestock production systems and 74% of students reported increased knowledge of coding and software, as well as their ability to clean, analyze, and make sense of raw data. The topics and delivery methods implemented in this course could inform the basis for development of similar interdisciplinary PLF courses at other academic institutions.

Analysis, modelling, and financial perspectives of reverse mortgage implementation in Colombia

PurposeIncreased life expectancy in recent years, together with the downward trend in fertility rates, will accelerate the aging of the Latin American population (Flamini and et al., 2018) [1]. This demographic change represents a problem for pension systems as the economic capacity to finance the retirement of the population could be insufficient in the future. This creates a favorable scenario for new financial products, including reverse mortgages. This study determines, from the supply approach, the potential of reverse mortgages in the main cities of Colombia as a complement to the financing of old age according to the guidelines of Benavides et al. (Benavides Franco et al., Jan. 2021) [2]. MethodologyThe following variables were used: housing prices, interest rates, mortality rates, and the consumer price index. The econometric testing simulates the possible paths of these variables, estimates the probability of occurrence of the paths, and calculates the expected losses of bidding banks. FindingsThe results show that reverse mortgages are a good complement to Colombia's pension system and confirm the feasibility of implementation at city level. It is recommended that the State stimulate the growth of this market and improve the corresponding regulatory framework. OriginalityThis study confirms the hypothesis that the reverse mortgages are a good complement to the pension system with city-level data. Specifically, it uses housing prices and mortality records from the main cities of the country (Bogotá, Cali, and Medellín) to capture their idiosyncrasies.

DL-RSM: Deep learning-integrated Response Surface Methodology for positive and negative-ideal environmental conditions estimation for crop yield

Understanding the most suitable and unsuitable environmental scenarios for crop production is critical to ensuring food security and sustainability. In this direction, the presented work proposes a novel approach, DL-RSM, to identify the optimized levels of environmental factors for wheat yield by integrating the Deep Learning technique with Response Surface Methodology (RSM). The uniqueness of this approach lies in its utilization of a 1-Dimensional Convolutional Neural Network model for response surface modelling. Additionally, the study has investigated the combination of numerical methods-based partial derivative calculation with a deep learning model. Specifically, our approach incorporates a symmetric difference quotient-based method for calculating partial derivatives, ultimately leading to the determination of optimal parameter values. Besides, multiple positive and negative ideal solutions are obtained from the modelled response surface using a modified gradient-based method. The effectiveness of our approach is illustrated through a case study on finding the optimized parameter values for the district-wise wheat yields of the Indo-Gangetic plains of India. The study results demonstrate the combinations of monthly rainfall levels and min. and max. temperature variations correspond to maximizing and minimizing wheat yield in the target region. The presented results provide significant insights into the relationship between wheat yields and the considered environmental factors that have not been sufficiently covered in the existing literature. The proposed methodology can benefit agricultural scientists, policymakers and farmers by offering an innovative approach to maximize crop yield and minimize resource utilization. Moreover, the study findings provide novel insights into the development of similar optimization strategies for other crops and systems.

New Perceptions of Ancient Commerce Driven by Underwater Ancient Site Investigations: A Case Study of Xinfeng River Basin

In the 1950s and 1960s, to address the flooding issues and power shortage that hindered national construction, the Xinfeng River hydropower plant was planned and built to prevent floods, store water, and generate electricity. Consequently, many ancient ruins in the study area were drowned, including ancient post roads, channels, villages, towns, bridges, and other relic sites. By checking historical data and adopting integrated underwater acoustic detection, we conducted a comprehensive cultural-relics survey on the flooded area under Wanlv Lake in the Xinfeng River Basin. A side-scan sonar detection of the underwater relics within the flooded area confirmed the spatial distribution of cultural relics in the Xinfeng River Basin. It portrayed ancient people’s production and life scenarios, outlined the migration and trade history within the region and beyond, and contributed to the enrichment of the literature and understanding of ancient shipping and trade in the basin.

A Blockchain-Based Real-Time Power Balancing Service for Trustless Renewable Energy Grids

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing produced vs consumed energy flows. In this context, being able to provide quick restore actions in response to unpredictable unbalancing events is a must, as fluctuations are the norm for renewable energy sources. To this aim, the high scalability and diversity of sources are crucial requirements for the said balancing to be actually manageable. In this study, we explored the challenges and benefits of adopting a blockchain-based software architecture as a scalable, trustless interaction platform between prosumers’ smart energy meters and the distributor. Our developed prototype accomplishes the energy load balancing service via smart contracts deployed in a real blockchain network with an increasing number of simulated prosumers. We show that the blockchain-based application managed to react in a timely manner to energy unbalances for up to a few hundred prosumers.

Fusion of cellulose microspheres with pulp fibers: Creating an unconventional type of paper

Cellulose microspheres (CMS) are a type of spherical regenerated cellulose particles with versatile properties which have been used as carrier materials in medical and technical applications. The integration of CMS into paper products opens up novel application scenarios for paper products in a wide range of fields. However, the incorporation of CMS carriers into paper products is challenging and hitherto no reports do exist in literature. Here, we present a feasibility study to incorporate up to 50 w.% CMS in paper hand sheets using retention aids. Our primary observations highlight the successful formation of uniform paper hand sheets retaining its tensile strengths at elevated CMS concentrations. Sheets with high CMS contents exhibit an increase in density and display enhanced surface smoothness — an outcome of a CMS layer forming atop the fiber base — which effectively bridges voids and rectifies surface irregularities as supported by Gurley testing, infinite focus microscopy and scanning electron microscopy. While our primary objective centered on the general feasibility to manufacture CMS-containing papers, the resulting composite scaffold carries significant potential as a platform for innovative, functional paper-based materials.

Optimal resource allocation in hierarchical organizations under uncertainty: An interval-valued n-person cooperative game approach

In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n-person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n-person cooperative games, an auxiliary nonlinear programming model and method are developed to solve the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods.

Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture

With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism with deep reinforcement learning (DRL) for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improved genetic programming and proximal policy optimization (IGP-PPO) which is a DRL method. Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events.

Diffusion of knowledge in the family milk production chain in the state of Rio de Janeiro

The milk food production chain is present throughout the country and has undergone many changes over time. It is extremely important to know and understand the current scenario of milk production, identifying the potential, limits and needs of this sector. The aim of this work is to research the family milk production chain in the state of Rio de Janeiro and analyze the actions taken to disseminate knowledge to small producers and the processes that could be optimized in terms of management and communication. The methodological approach used in this work is qualitative, bibliographical and exploratory. As this research is linked to the field of management, the qualitative approach was chosen because it is considered the most appropriate for understanding the subject. It is exploratory because it set out to research the family dairy chain, its characteristics, the state of the art and the diffusion and dissemination of knowledge. It was concluded that there is a need for training and capacity building in the face of the growing amount of information and knowledge that needs to be managed between the different players in the production process.

Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach

The growing energy demand and concerns over fossil fuel consumption underscore the need for a shift to renewable energies and biofuels. Concurrently, the rising global population and increased biofuel demand strain the food supply system, requiring enhanced production capabilities. By using a system dynamics model, this study investigates the intricate relationship between bioethanol production and food security in Australia, emphasizing the essential balance for a sustainable bioethanol supply chain. The research explores the interplay among population, food supply, and energy supply subsystems, projecting diverse bioethanol production scenarios until 2050. Findings reveal constraints within the Australian bioethanol supply chain, with current capacity meeting demand at a 1.5 % bioethanol blend rate to gasoline. However, surpassing 3.4 % demands significant capacity expansions incurring substantial costs. At a 5 % blend rate, it is imperative to augment the production capacity to 1275 million liters of bioethanol by 2050. The study underscores the limitations of relying on sorghum for bioethanol production, emphasizing the strategic need to embrace alternative sources such as wheat starch waste and molasses. Addressing the challenge of supplying sufficient food for consumption and energy production, our research advocates for a shift towards fuels produced without edible resources. These insights are vital for policymakers and industry stakeholders, contributing to the understanding of complexities in establishing a sustainable bioethanol industry while ensuring food security and environmental sustainability.

Unravelling Resilience in Homestead Agroforestry Practice: A Review

The unprecedented climate changes are impacting agrarian livelihoods worldwide. Since lives heavily depend on agriculture, alternative farming mechanisms should be promoted to counter the effects. Homestead agriculture has a long tradition of compensating for the vagaries of weather and offering subsistence to the growers, at the same time conserving the ecosystem. The resilience of the practice is reviewed in this article with a focus on agroforestry’s unique characteristics, high biodiversity and multifaceted benefits as a land use system. A better understanding of the resilience potential of the homesteads to varying environments is important to integrate the system into the agricultural production scenario of nations, thereby bridging the unpredictable events of climate change. The best illustration of an effective indigenous resilience strategy is the tenacious persistence of homestead farming across millions of hectares.

Carbon and water footprint of battery-grade lithium from brine and spodumene: A simulation-based LCA

Increasing demand for lithium driven by e-mobility spurs the expansion of lithium projects and exploration of lower-grade resources. This article combines process simulation (HSC Chemistry) and life cycle assessment tools to develop life cycle inventories considering declining ore grades scenarios for battery-grade Li2CO3 production from pivotal sources and regions (Salar de Atacama - brine and Greenbushes - spodumene). Depending on the ore grades, climate change results range from 5,0 to 25,0 kgCO2eq/kg Li2CO3 for brine and from 17,1 to 22,3 kgCO2eq/kg Li2CO3 for spodumene. Water consumption for brine varies from 0,2 to 7,7 m3/kg Li2CO3, depending on flow accounting, while for spodumene, it ranges from 0,2 to 0,5 m3/kg Li2CO3. The research shows that decreasing ore grade can lead to a fivefold increase in the carbon footprint for brine-based production and a 1.3-fold increase for spodumene. This underscores the importance of decarbonizing energy provision for lithium production to guarantee a sustainable battery supply chain. This research offers insights into using process simulation to enhance existing LCA studies in the raw materials industry and further develop inventory datasets, considering variations such as deposit ore grades.

Cellulose nanofibre films as a substitute for plastic packaging: A comparative environmental life cycle assessment

Accumulation of synthetic food packaging in the natural environment has increased considerably in last few decades, posing serious concerns to human and aquatic life. Hence, substituting synthetic packaging with biobased packaging is one viable option. Cellulose nanofibres can easily be transformed into films, which could be a suitable alternative for petroleum-derived packaging. However, production of these films consumes a significant amount of energy as compared with the synthetic packaging. The main contributors for this high energy consumption are associated with the treatment of pulp, production of fibres and drying of films. This study analyzes the environmental impacts (in terms of energy and water) of spray deposited cellulose nanofibre films and its key drivers through an attributional cradle-to-gate life cycle assessment. Different scenarios were formulated and studied in terms of waste feedstock choice, solid content, forming composite of cellulose nanofibres, and location (Australia, China, Germany, Great Britain and United States of America). The results indicate that the environmental impacts of spray deposited cellulose nanofibre films based on a baseline scenario for a large-scale production were still higher as compared with petroleum-based synthetic packaging. However, establishing a recommended scenario from the studied scenarios could lower the environmental impacts significantly (two to five times) in comparison to synthetic food packaging. This life cycle assessment model has the potential to support the reduction of the environmental impact of the studied green chemistry option and to accelerate the transition towards more sustainable packaging.