Optimization Of Production Process Research Articles

DETERMINANTS OF THE FORMATION OF CHINA'S SMART ECONOMY

The development of China's smart economy has been heavily influenced by the government's strategic policies and investments in key technologies. One of the key technologies driving China's smart economy is the Internet of Things (IoT), which involves connecting machines, devices and sensors to the Internet for real-time data collection and analysis. This enables the optimisation of production processes and improved efficiency in industries such as manufacturing. The Chinese government has actively promoted the adoption of IoT technologies in manufacturing through policies such as the Made in China 2025 plan and the Internet Plus strategy. Another essential technology for China's smart economy is big data, which enables intelligent decision-making in various industries such as finance, healthcare and transportation. The government has encouraged the development of big data infrastructure, including data centres and cloud computing, to support the growth of the industry. Initiatives such as the National Big Data Comprehensive Pilot Zone have been established to promote the development of the big data industry. Artificial intelligence (AI) is also a key technology for China's smart economy, and the government has made significant investments in AI research and development. In 2017, China announced its plan to become a world leader in AI by 2030, with the aim of building a domestic AI industry worth $150 billion. The government has launched initiatives, such as the AI Development Plan, to build an AI ecosystem and promote the integration of AI with traditional industries. Robotics is another critical technology for China's smart economy, particularly in manufacturing. The government has encouraged the use of robots in manufacturing through tax incentives and subsidies, making China the world's largest market for industrial robots. The government aims to further develop the robotics industry to meet the needs of China's smart economy. In conclusion, China's smart economy is driven by strategic policies and investments in key technologies such as IoT, big data, AI and robotics. The government has launched various initiatives to support the development and integration of these technologies with traditional industries, with the aim of building a sustainable and innovative smart economy. These technologies play a critical role in China's industrial upgrading and transformation, and their continued development and adoption are expected to shape the future of China's economy and global competitiveness.

Harnessing the Power of IoT and AI for Human Evolution

The internet of things (IoT) and artificial intelligence (AI) are two of the most transformative technologies of the modern era. They have the potential to change how we live, work, and interact with each other. This paper explores the impact of IoT and AI on human evolution, including the challenges and opportunities they present. Through a review of relevant literature, this paper identifies the potential benefits of IoT and AI, such as improving healthcare, increasing productivity, and promoting sustainability. However, this paper also highlights the ethical and social issues that need to be addressed to ensure that these technologies benefit humanity. (Manyika, et al., 2015)The Internet of Things (IoT) and Artificial Intelligence (AI) are two transformative technologies that are revolutionizing the way we live, work, and interact with each other. Together, they have the potential to enable unprecedented levels of connectivity, automation, and intelligence, leading to significant improvements in various aspects of human life, from healthcare and education to transportation and energy. In this research paper, we explore the latest developments in IoT and AI and their potential impact on human evolution. We examine how these technologies are being applied in different fields, such as smart homes, cities, factories, and healthcare, and we discuss the opportunities and challenges associated with their adoption. We also explore how IoT is being employed in agriculture to enhance crop yields and in transportation to optimize traffic flow and reduce emissions. We also investigate how AI is transforming the field of healthcare by enabling personalized treatments and improving patient outcomes, as well as how it is being used in education to provide customized learning experiences. Additionally, we discuss the role of IoT and AI in creating smart cities that are more efficient, sustainable, and liveable, and how they are being used in industry to optimize production processes and reduce waste. Through these examples, we demonstrate the potential of IoT and AI to drive human evolution and shape the future of our society. Moreover, we explore the ethical and societal implications of IoT and AI and provide recommendations for their responsible and sustainable development. Ultimately, we argue that harnessing the power of IoT and AI can contribute to human evolution by enhancing our ability to understand and address complex problems, enabling us to live healthier, safer, and more fulfilling lives, and empowering us to create a more equitable and sustainable world.

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel.

Properties of Fermented Beverages from Food Wastes/By-Products

Current global research aims to explore the key role of diet and understand the benefits of a balanced diet. Furthermore, many authors have pointed to the added value of by-products as a solution to make food production chains more environmentally and economically sustainable. By-products emerge as an alternative matrix to fermentation, and the fermentation process has the potential to transform by-products into value-added products through an efficient and sustainable process. During fermentation, besides the consumption of molecules to grow, microbial enzymes act on several phytochemical compounds, creating new derivative compounds that affect the flavour and function of fermented beverages. As an alternative for consumers with lactose intolerance or vegan or vegetarian diets, new beverages produced from plant by-products and probiotic bacteria hold great promise for the global functional food market. Several challenges were overcome in developing these new products from by-products, namely the availability and quality/standardization of raw materials, adapted microbial starter cultures for fermentation, and optimization of production processes to maximize consumer acceptance and product yield. This review provides an overview of recent research/developments in the field of new fermented beverages from by-products, and aspects related to their functionality, beyond the challenges of these new beverages.

Особливості розвитку міжнародних логістичних систем за умов глобалізації

This research paper is devoted to studying the essence and features of international logistics systems, which are an es-sential factor in the competitiveness of enterprises and countries in the globalization of the world economy. The authors analyze the role and functions of international logistics systems in ensuring effective material management and information flows between subjects of international economic cooperation. The article identifies the main types of international logistics systems by regulatory bodies, forms of organization, types of logistics operations, and scope of activities. The advantages and disadvantages of international logistics systems for enterprises and participating countries are also considered, and the main problems and barriers to their development are identified. Based on the analysis, the author proposes directions for improving international logistics systems, considering the specifics of industries and regions. The article reviews globaliza-tion processes and established practices of international logistics systems. The formation of logistics processes in Europe and Ukraine is shown. The existing scientific and practical approaches and principal components of transport logistics are sum-marized. The role of business process reengineering in it is determined for the successful implementation of which an appro-priate algorithm of the sequence of actions should be implemented. The author outlines the terms, methods of delivery, and operations with customers that are common to the Ukrainian logistics services market. The author identifies the place and outlines the prospects for a resumption of activities of domestic enterprises in the face of changing external environment. In this sense, the successful experience of leading international enterprises indicates the need to address several issues related to optimizing production processes. The proposed directions for the restoration of the logistics business in the post-war period include refutation and unification of standards and principles of international law, which will regulate the issues of cargo transportation in terms of various modes of transport and optimize the implementation of logistics operations and will allow forming a system of the transparent and convenient international logistics market. There also appears to be a need for a di-rection designed to ensure the efficiency of supply chains by logistics companies, which in turn covers a set of different types of flows and automated sequential business processes, dramatically simplifies, and makes delivery services transparent, optimizes consumer demand, expands the network and facilitates access to foreign markets. In this aspect, the role of e-commerce should be given due credit, which is the impetus for the existence of supply chains and transparency of logistics center management. Keywords: logistics, transport infrastructure, warehouses, competition, logistics outsourcing, logistics infrastruc-ture, business process reengineering, supply chains, e-commerce.

Transformation of Global Value Chains in the Context of Modern Macroeconomic Shocks

Purpose: The aim of this study is to examine the concept of global value chains (GVCs) in terms of the distribution of benefits among participating countries by deepening the international division of labor and including geographically distant and differently developed countries. Theoretical framework: The article emphasize that the involvement of countries in GVCs, along with benefits, has several risks associated with both endogenous and exogenous factors. Design/methodology/approach: The authors analyzed GVC studies and used such research methods as a case study and factor, comparative, and structural analysis. Russian and international statistical data were used as the study materials. Findings: The results of the analysis indicate that exogenous factors related to the COVID-19 pandemic and geopolitical tensions have led to the breakdown of GVCs and increased their instability. To address these issues, the authors suggest increasing the domestic value added in industries that rely heavily on foreign resources, reforming GVCs by localizing partnerships through deglobalization processes, and seeking out new trading partners. Research, Practical & Social implications: The study authors propose to make changes to the current structure of GVCs. This can help minimize negative consequences for the economy through the introduction of practices aimed at increasing the number of suppliers, expanding sales markets, and optimizing production processes. Originality/value: The value of the study lies in its identification of both the advantages and challenges of GVC participation for countries. It also offers practical solutions to enhance the resilience of GVCs, making the study results relevant for policymakers, businesses, and industries involved in GVCs.

Process intensification and optimization for efficient production of dimethyl carbonate through urea alcoholysis using low-cost anhydrous zinc acetate catalyst

Anhydrous zinc acetate was used as a low-cost catalyst for the urea alcoholysis to produce dimethyl carbonate (DMC). Important operating variables including reaction time, temperature and catalyst amount were optimized using response surface methodology. Optimization was assessed through two different approaches. In the first approach, only DMC yield was maximized. N-methyl methyl carbamate (NMMC, the unfavorable byproduct) yield was minimized along with maximizing DMC yield in the second approach. Sobol sensitivity analysis uncovered that reaction temperature and the binary interaction of reaction temperature/catalyst amount were the most influential parameters on DMC yield with 57.01% and 29.17% impacts, respectively. Moreover, temperature and reaction time with 61.13% and 19.74% impacts were the most effective variables on NMMC yield. The multi-objective optimization results were more suitable for application in continuous production of DMC due to avoiding NMMC production. A modified continuous process was also proposed for DMC production. The main advantage of the preposed process was immediate extraction out of DMC from the reaction zone. To investigate the possible reaction mechanism, the Binding Energies (BEs) of the reactants were assessed using Density-Functional-Theory. Results illustrated that the highest BE values belong to the interaction of zinc with the nitrogen of methyl carbamate (MC).

PENGARUH PENERAPAN ACTIVITY BASED COSTING (ABC) TERHADAP MANAJEMEN BIAYA PRODUKSI, PRODUKTIVITAS PERUSAHAAN, DAN EFISIENSI PERUSAHAAN

<p align="justify">This study aims to analyze the influence of the implementation of activity-based costing (ABC) on the management of production costs, productivity and efficiency of the company. The research method used is qualitative with case studies in several manufacturing companies that have implemented the ABC system. Data collection is done through in-depth interviews with production managers, analysis of financial statements and direct observation of the production process. This study was conducted for 6 months, from January to June. The results show that the implementation of ABC has a significant impact on the company's production cost management. With ABC, companies can more accurately determine the cost of production for each activity involved, helping to make resource allocation decisions and control costs more accurately. Furthermore, the adoption of ABC is also associated with increasing the company's productivity. With a better understanding of production costs, companies can optimize production processes and improve overall operational efficiency. However, this study has some limitations, including limited case study and focus on qualitative data. The implication of this study is the importance of how companies view the implementation of ABC as a tool to improve cost management, productivity and operational efficiency in order to gain a competitive advantage. </p><p> </p><p><strong>Keywords</strong>: Activity Based Costing, Production Cost Management, Company Productivity, Company Efficiency </p>

A Research on Rheological Behavior of Aqueous Solutions of Pomegranate Peel Powder

Determining the viscosity of pomegranate peel powder (PPP) can aid in product development, process optimization, quality control, ingredient interactions, and scientific research related to pomegranate peel-based products or formulations. Therefore, in this study, the rheological behavior of dried and ground pomegranate peel has been investigated. The apparent viscosities of pomegranate peel solutions prepared with distilled water are measured by a rotational viscometer. The viscosity of the pomegranate peel solution at each concentration is measured at shear rates varying between 2.64 s-1 and 22 s-1 at constant temperature (25 °C). When the effect of concentration on apparent viscosity has been examined, it is observed that the viscosity rises with increasing concentration at a constant shear rate. Among the rheological models studied, Power model provides a good fit for the experimental viscosity data of aqueous solutions of pomegranate peel powder at different concentrations. The consistency coefficient and the flow behavior index are calculated using the power law model and the solutions have been found to exhibit pseudoplastic behavior.

Understanding the effect of temperature downshift on CHO cell growth, antibody titer and product quality by intracellular metabolite profiling and in vivo monitoring of redox state.

The strategy of temperature downshift has been widely used in the biopharmaceutical industry to improve antibody production and cell-specific production rate (qp ) with Chinese hamster ovary cells (CHO). However, the mechanism of temperature-induced metabolic rearrangement, especially important intracellular metabolic events, remains poorly understood. In this work, in order to explore the mechanisms of temperature-induced cell metabolism, we systematically assessed the differences in cell growth, antibody expression, and antibody quality between high-producing (HP) and low-producing (LP) CHO cell lines under both constant temperature (37°C) and temperature downshift (37°C→33°C) settings during fed-batch culture. Although the results showed that low-temperature culture during the late phase of exponential cell growth significantly reduced the maximum viable cell density (p < 0.05) and induced cell cycle arrest in the G0/G1 phase, this temperature downshift led to a higher cellular viability and increased antibody titer by 48% and 28% in HP and LP CHO cell cultures, respectively (p < 0.001), and favored antibody quality reflected in reduced charge heterogeneity and molecular size heterogeneity. Combined extra- and intra-cellular metabolomics analyses revealed that temperature downshift significantly downregulated intracellular glycolytic and lipid metabolic pathways while upregulated tricarboxylic acid (TCA)cycle, and particularly featured upregulated glutathione metabolic pathways. Interestingly, all these metabolic pathways were closely associated with the maintenance of intracellular redox state and oxidative stress-alleviating strategies. To experimentally address this, we developed two high-performance fluorescent biosensors, denoted SoNar and iNap1, for real-time monitoring of intracellular nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide + hydrogen (NAD+ /NADH) ratio and nicotinamide adenine dinucleotide phosphate (NADPH) amount, respectively. Consistent with such metabolic rearrangements, the results showed that temperature downshift decreased the intracellular NAD+ /NADH ratio, which might be ascribed to the re-consumption of lactate, and increased the intracellular NADPH amount (p < 0.01) to scavenge intracellular reactive oxygen species (ROS) induced by the increased metabolic requirements for high-level expression of antibody. Collectively, this study provides a metabolic map of cellular metabolic rearrangement induced by temperature downshift and demonstrates the feasibility of real-time fluorescent biosensors for biological processes, thus potentially providing a new strategy for dynamic optimization of antibody production processes.

Multi-objective optimization of hydrogen production process and steam reforming reactor design

This study proposes a multi-objective optimization approach to design a steam methane reforming (SMR) reactor and maximize the efficiency of the hydrogen production process. Out of 1782 possible variable combinations, only 50 iterations were performed, identifying three Pareto optimal that resulted in reactor size reductions of 50.53%, 35.56%, and 20.69%, respectively, compared to the reference reactor. The process efficiency for each optimal design varied slightly, with one achieving a 105.05% increase in efficiency, another remaining stable at 100.48%, and a third experiencing a slight decrease to 86.66% compared to the reference case. The results offer practical insights for planning an on-site distributed hydrogen production system, demonstrating that an increase in overall process efficiency can be achieved even with a reduced reactor size. This work is the first attempt to optimize a hydrogen production system by simultaneously considering overall process efficiency and SMR reactor design.

Leveraging Software Automation to Transform the Manufacturing Industry

The manufacturing industry is undergoing a paradigm shift driven by advancements in software automation technologies. This paper explores the transformative impact of leveraging software automation on various facets of the manufacturing sector. As industries strive for increased efficiency, cost-effectiveness, and agility, the integration of automation solutions has emerged as a key strategy.&#x0D; The paper begins by providing an overview of the current landscape of the manufacturing industry, highlighting the challenges faced in terms of manual processes, operational bottlenecks, and the need for enhanced productivity. It then delves into the role of software automation in addressing these challenges, showcasing how technologies such as robotic process automation (RPA), artificial intelligence (AI), and industrial IoT are revolutionizing traditional manufacturing workflows.&#x0D; Furthermore, the discussion covers the benefits of software automation in optimizing production processes, reducing errors, and enhancing overall quality. Real-world case studies and examples illustrate successful implementations of automation across diverse manufacturing sub-sectors, showcasing the tangible advantages experienced by companies that have embraced this transformative approach.&#x0D; The paper also explores the implications of software automation on the workforce, emphasizing the need for upskilling and reskilling initiatives to ensure a smooth transition to an automated manufacturing environment. It addresses concerns related to job displacement and emphasizes the potential for creating new roles that harness the strengths of human-machine collaboration.&#x0D; In addition, the paper discusses the impact of software automation on supply chain management, emphasizing how it contributes to improved demand forecasting, inventory management, and logistics optimization. The resulting supply chain efficiencies contribute to a more resilient and responsive manufacturing ecosystem.&#x0D; The conclusion of the paper summarizes key findings and outlines future prospects for the continued integration of software automation in the manufacturing industry. As technology continues to advance, embracing automation becomes not only a strategic imperative but also a means to foster innovation, sustainability, and competitiveness in an evolving global marketplace.

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Production optimization is a complex process because it must take into account various resources of the company and its environment. In this process, it is necessary to consider the enterprise as a whole, taking into account the interaction between its key elements, both in the technological and business layer. For this reason, the article proposes the use of enterprise architecture, which facilitates the interaction of these layers in the production optimization process. As a result, a proprietary meta-model of enterprise architecture was presented, which, based on good practices and the assumptions of enterprise architecture, facilitates the construction of detailed optimization models in the area of planning, scheduling, resource allocation, and routing. The production optimization model formulated as a mathematical programming problem is also presented. The model was built taking into account the meta-model. Due to the computational complexity of the optimization model, a method using an artificial neural network (ANN) was proposed to estimate the potential result based on the structure of the model and a given data instance before the start of optimization. The practical application of the presented approach has been shown based on the example of optimization of the production of an exemplary production cell where the cost of storage and the number of unfulfilled orders and maintenance are optimized.

Optimization of production process scheduling at Mataram Convection using the Campbell-Dudek and Smith method and the Ho and Chang method

Konveksi Mataram (Djagoan Kaos dan Seragam) is one of the industries engaged in the manufacture of various types of clothing models with fabric as the basic material. So far, the scheduling method used by the company is the First Come First Serve method, in which the completion of production is based on order-to-order data. In this case, with high order intensity, companies often experience difficulties in completing orders according to a predetermined pick-up time. The problems experienced by the company were caused by the production process scheduling that was not optimal. Based on the problems encountered, the purpose of this research is to obtain the optimal scheduling sequence by determining the smallest makespan (minimum total completion time) of the application of the method to the production process. The methods used in this study are the Campbell-Dudek and Smith method and the Ho and Chang method and from these two methods, it is known that the smallest production process is optimal. Based on the results of calculations using the Campbell-Dudek and Smith method, the optimal scheduling sequence with the smallest makespan is 39163 minutes or the production process will be completed in 73 working days. While the results of calculations using the Ho and Chang method obtained the optimal scheduling sequence with the smallest makespan of 38660.50 minutes or the production process will be completed in 72 working days. From the makespans of the two methods, the Ho and Chang method is superior to the Campbell-Dudek and Smith method with a difference of 502.50 minutes or about 1 working day, whereas when compared to the company's initial method, namely First Serve First Come with a makespan of 43025.50 minutes, the HC method can make completion time efficient with a difference of 4365 minutes or about 8 working days.&#x0D; Keywords: Campbell-Dudek and Smith methods, first come first serve, Ho and Chang, makespan, production schedulingMSC2020: 90B30

The impacts of Industry 4.0 enabling technologies on operational strategy

Purpose: Adapting to technological advances is a pertinent challenge for operations in the Manaus Industrial Park. This study aims to address the following research question: how do Industry 4.0 (I4.0) enabling technologies impact the quality and flexibility strategy in the Manaus Industrial Park? Originality / Value: This study developed a systematic and structured way to understand the benefits of I4.0 technologies in quality strategies and flexibility of manufacturing production lines. Manaus Industrial Park plays an essential role in the development of innovations in the Amazon region, and companies must be competitive in this new context of digitization of manufacturing. Methods: This study used a qualitative and descriptive approach. Data were collected through semi-structured interviews with industrial managers from 12 companies that operate in the Manaus Industrial Park. Results: The results of this work point to a frequent use of computer systems to integrate machines in operations, such as the use of M2M technology and cyber-physical systems. The companies researched in the Manaus Industrial Park are adopting essential technologies to create the infrastructure for the I4.0 implantation. This is the first step to making smart factories in the future. Conclusions: Industry 4.0 enabling technologies affect operations' quality and flexibility strategy, promoting cost savings by optimizing production processes.

Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability

To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution–recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of reservoir property fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In this closed-loop optimization framework, production optimization, based on the differential evolution (DE) algorithm, and data assimilation, based on the iterative ensemble smoother (IES), are performed alternately to achieve a real-time well control optimization and to estimate reservoir properties (e.g. permeability) as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimates is adopted to consider geologic uncertainty in the optimization process. Geothermal reservoir production cases are examined to evaluate the performance of the proposed closed-loop optimization framework. Our results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process.

Optimization of Biofuel Production Process Using Design of Experiments (Doe)

This study focuses on optimizing the process of biofuel production from citrus peel using the Design of Experiments (DOE) technique. This study aims to determine the optimal values for the variables that have a significant impact on the production of biofuel. The variance within and between data groups was determined using the analysis of variance (ANOVA) table. The ANOVA table shows how much of the response variable's variation (biofuel production) can be explained by the independent variables (A, B, C, D, E, AB, AC, AD, AE, and BJ) and how much is caused by random error. The ANOVA table comprises of three primary parts: the F-statistic, the p-value, the df, the mean square (MS), the source of variation, and the sum of squares (SS). The wellspring of variety alludes to the beginning of the information variety, which can be either the lingering or the model. The amount of squares estimates the information's changeability, with the absolute amount of squares addressing the amount of the squared deviations of the genuine qualities from the mean worth. The residual is the sum of the squared deviations from the predicted values of the actual values, while the model's sum of squares is the sum of the squared deviations from the mean of the predicted values. The model has 10 degrees of freedom (the number of independent variables) and the residual has 4 degrees of freedom (the number of observations minus the number of independent variables). These degrees of freedom represent the number of independent pieces of information used to estimate a parameter. The mean square, which indicates the typical amount of variation for each variation source, is calculated by dividing the sum of squares by the degrees of freedom. The degree to which the model explains the variation in the data is indicated by the F-statistic, which is the ratio of the model's mean square to the residual's mean square. The probability of obtaining an F-statistic that is as large as the one observed if the null hypothesis is true is represented by the p-value. The independent variables' insignificant impact on biofuel production is the null hypothesis in this instance. The model's p-esteem in this study is under 0.05, demonstrating that the free factors essentially affect biofuel creation and that the model is genuinely huge. In addition, the model is significant because the F-statistic is relatively large in comparison to the F-distribution for the 10 and 4 degrees of freedom, respectively. The estimated coefficients for the linear regression model used to investigate the production of biofuel from citrus peel can be found in the ANOVA coefficients table. The table provides a list of the intercept and independent variables' coefficients, standard errors, t-values, and p-values. When all of the independent variables are zero, the intercept has a coefficient of 0.0672, indicating the estimated value of the response variable. The fact that the intercept does not differ significantly from zero is supported by the fact that its p-value is not significant. The fact that the coefficients of the independent variables A, E, AC, AD, AE, and BJ are not statistically significant indicates that these variables have little impact on the response variable. On the other hand, the positive coefficients and significant p-values of the independent variables B and C suggest that an increase in their values could result in an increase in the production of biofuel from citrus peel. In conclusion, the key variables that influence the production of biofuel from citrus peel have been identified thanks to the use of the Design of Experiments (DOE) method. According to the findings of this study, an increase in the production of biofuel from citrus peel may result from an increase in the values of the independent variables B and C. The development of environmentally friendly energy sources and the optimization of biofuel production processes will benefit greatly from these findings

Characterizing and utilizing oxygen-dependent promoters for efficient dynamic metabolic engineering

Promoters adjust cellular gene expression in response to internal or external signals and are key elements for implementing dynamic metabolic engineering concepts in fermentation processes. One useful signal is the dissolved oxygen content of the culture medium, since production phases often proceed in anaerobic conditions. Although several oxygen-dependent promoters have been described, a comprehensive and comparative study is missing. The goal of this work is to systematically test and characterize 15 promoter candidates that have been previously reported to be induced upon oxygen depletion in Escherichia coli. For this purpose, we developed a microtiter plate-level screening using an algal oxygen-independent flavin-based fluorescent protein and additionally employed flow cytometry analysis for verification. Various expression levels and dynamic ranges could be observed, and six promoters (nar-strong, nar-medium, nar-weak, nirB-m, yfiD-m, and fnrF8) appear particularly suited for dynamic metabolic engineering applications. We demonstrate applicability of these candidates for dynamic induction of enforced ATP wasting, a metabolic engineering approach to increase productivity of microbial strains that requires a narrow level of ATPase expression for optimal function. The selected candidates exhibited sufficient tightness under aerobic conditions while, under complete anaerobiosis, driving expression of the cytosolic F1-subunit of the ATPase from E. coli to levels that resulted in unprecedented specific glucose uptake rates. We finally utilized the nirB-m promoter to demonstrate the optimization of a two-stage lactate production process by dynamically enforcing ATP wasting, which is automatically turned on in the anaerobic (growth-arrested) production phase to boost the volumetric productivity. Our results are valuable for implementing metabolic control and bioprocess design concepts that use oxygen as signal for regulation and induction.

Optimization of the hydrogen production process coupled with membrane separation and steam reforming from coke oven gas using the response surface methodology

As a by-product of the coking industry, coke oven gas (COG) is often burned as fuel gas. However, COG contains a large amount of hydrogen. If hydrogen is recovered, the economic and environmental benefits of the coking industry can be improved. In this work, a novel automotive fuel cell hydrogen production process coupled with membrane separation (PRISM® Membrane Separator, polyimide hollow fiber membrane), pressure swing adsorption (PSA) and methane steam reforming (SMR) is proposed. The process uses membrane separation and PSA to produce high-purity hydrogen for fuel cells. The methane-rich membrane residue gas is used as the feed gas of the reforming reactor. The hydrogen-containing gas at the outlet of the reactor enters the membrane separator to further improve the hydrogen yield. In addition, based on the principle of temperature gradient utilization, the high-temperature gas at the outlet of the reactor is used as the heat source for the system. Through a synergy of separation and reaction, the process supports the low-cost and efficient production of fuel cell hydrogen from COG. The proposed process was established using UniSim Design. The key variables were determined by sensitivity analysis, and the response surface methodology based on Box–Behnken design (BBD) method was further used to optimize the key variables. The optimal operating variables are as follows: the area of the third hydrogen membrane (HM3) is 3000 m2, the reaction temperature is 917 K and the molar ratio of steam to methane (S/C) is 1.12. The cost of the hydrogen production process is 1.56 $/kg, which means it has good prospects for use in industrial applications.

Scaled-Up Preparation of an Intermediate of Upatinib, ACT051-3

Upatinib, a Janus kinase inhibitor drug, was developed by a biotech company to treat immune diseases. The compound tert-butyl (5-toluenesulfonyl-5h-pyrrole [2,3-b] pyrazine-2-yl) carbamate (ACT051-3) is an important intermediate of Upatinib. To date, the steady industrial production of this intermediate compound (ACT051-3) has not been reported. In this study, we described the specific synthesis method and process of the compound ACT051-3 in terms of laboratory synthesis, pilot scale-up, and industrial production. During the exploration of the process route for ACT051-3, many appropriate adjustments and improvements were made to the reaction conditions, finally leading to the successful development of the optimal industrial production process for ACT051-3. The reaction time was nearly doubled by changing the state of the potassium carbonate involved in the reaction, which greatly improved the reaction efficiency. Additionally, by introducing N,N-diisopropylethylamine (DIPEA) to the reaction, the amount of the expensive catalyst Pd(OAc)2 was reduced 2.5-fold, significantly lowering production costs, confirming the feasibility of this process route and the industrial production of ACT051-3, and satisfying market demand for this important intermediate.