Optimization Of Production Process Research Articles

Esterification and Transesterification Optimization Processes of Nonedible (Castor and Neem) Oils for the Production of Biodiesel

In current times, the diminishing reserves of petroleum, increased energy consumption across various sectors, and their consequential environmental impact have become apparent. Consequently, it is necessary to develop sustainable and eco-friendly energy sources to meet growing demands. The article aimed to blend castor and neem oils (in a 50:50 ratio) to rectify the drawbacks present in castor biodiesel such as elevated kinematic viscosity and density. Response surface methodology was used to study the optimization of the two-step biodiesel production process through the use of a central composite design (CCD). For the esterification step, a methanol-to-oil molar ratio of 7.5:1, 1.75 wt.% of H2SO4, and a temperature of 55 °C were optimal. In the transesterification step, optimized conditions included a methanol-to-oil molar ratio of 9:1, 2.50 wt.% of calcium oxide, a temperature of 55 °C, and a stirring speed of 900 rpm, resulting in a 93% yield of methyl ester. Different properties of produced biodiesel were examined using the standard values provided by EN 14214 and ASTM D6751. The production of biodiesel from a mixture of castor and neem oils did not have any adverse impacts on food security.

Trends and innovations in mycorrhizal biofertilizers: a technological prospecting study

Exponential population growth, combined with the reduction of arable land, has created a need for increased agricultural production within a smaller physical space and with limited water and nutrient resources. In this context, biofertilizers have emerged as an environmentally friendly alternative to meet the demand for technologies and products that enhance productivity. The objective of this study was to map the production of technological knowledge and identify the main characteristics of innovation assets developed in the field of biofertilizers, with a particular focus on mycorrhizal biofertilizers, during the first decades of the 21st century. To achieve this, data was collected from international databases, including the European Patent Office (EPO) and the World Intellectual Property Organization (WIPO), as well as from the national database of the National Institute of Industrial Property (INPI) in Brazil. The search was conducted using simple and combined queries with the keywords "Mycorrhiza" and "Biofertilizer," followed by quantitative and qualitative analyses. The results indicated that the primary patent applicants in this field are companies and universities, predominantly located in North America and Asia. The highest concentration of patent publications occurred between 2010 and 2021, particularly in relation to new biofertilizer compositions and the optimization of production processes for these compounds. Although new technologies in the field of mycorrhizal biofertilizers can be observed, there are still unexplored avenues, and new initiatives will be important for the emergence of innovative solutions.

Application of the integrated well-surface facility production system for selecting the optimal operating mode of equipment

This work is dedicated to the analysis and development of an integrated production system that combines wells and surface facilities to select the optimal operating mode for equipment. The main focus is on studying data analysis methods, as well as collecting and processing information, which ensures high accuracy in process control and optimal use of technological capabilities. The integration of data from various levels of the production chain, from the well to the surface facilities, opens up new opportunities for optimizing equipment performance and improving resource management quality. Integration of wells and surface facilities – The effective integration of wells and surface facilities is crucial for optimizing production processes, minimizing operational costs, and reducing environmental impact. Integrated management systems allow the automation of many processes, providing continuous monitoring of operating parameters, automatic adjustment of settings, and supplying data for quick decision-making. This includes real-time data collection, analysis, and the use of the obtained information to select optimal operating modes, which helps improve both overall efficiency and safety in production processes. In the modern oil and gas industry, the efficient use of equipment at all stages of hydrocarbon production is of particular importance. Optimizing the operation of wells and surface facilities is a key aspect for increasing economic efficiency and reducing environmental impact. In this regard, the development and application of integrated systems that enable real-time control and adaptation of equipment operating modes has become a relevant and significant task.

Multi-Criteria Optimization of the Paper Production Process Using Numerical Taxonomy Methods: A Necessary Condition for Predicting Heat and Electricity Output in a Combined Heat and Power (CHP) System

The subject of this study is the optimization of the paper production process in one of Poland’s leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (Vs) and winding speed (Vn) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes.

The role of the construction industry in the recovery of processing enterprises during the crisis

The article discusses the consequences of the crisis period for the work of processing enterprises in Ukraine. It is established that hostilities on the territory of our state and general instability lead to a decrease in production and force processing enterprises to cease their activities. Many enterprises are experiencing serious financial difficulties due to work restrictions, problems with the supply of raw materials, and the loss of industrial facilities. In the summer of 2022, the percentage of companies that closed or were in the process of ceasing operations was 46.8 %. A significant consequence of the war for the Ukrainian economy at the moment is the rapid changes in the territorial location and the change in the symmetry of the processing industry of Ukraine. The war also affected the sectoral structure of the processing industry, producing significant shifts and changes in the volume of products sold. The current political situation in Ukraine and the severance of business chains with the temporarily occupied territories require processing enterprises to search for new markets for products, sources of supply of raw materials, change logistics links, increase the production of raw materials and their own products. During the study, it was found that certain key aspects of the impact on the activities of processing enterprises were previously insufficiently studied. It is determined that the construction industry is of critical importance for the processing industry of the State during the crisis, which is designed to contribute to the recovery of this industry: to develop infrastructure, to build and modernize production facilities, which allows to introduce new technologies and optimize production processes, to improve the conditions of production itself, to build energy facilities to provide processing enterprises with stable and affordable energy supply, to apply modern construction technologies that will create environmentally friendly and energy-efficient production. For the restoration and development of the processing industry of Ukraine, taking into account the impact of the construction industry, we propose to apply the following measures: widespread implementation of the enterprise relocation program with the expansion of state support; the use of state programs for the reconstruction of property and lending, the development of international communications and the European vector for the recovery of the post-war industry based on the ESG concept and the principles of building back better, compliance with the principles of energy efficiency and energy saving, and the application of decarbonization measures. These tools are designed to provide stimulating processes in the processing industry to achieve and exceed the prewar level of the volume of competitive and high-quality products. The development of the construction industry plays an important role in the restoration of processing enterprises in Ukraine, so it is important to create favorable conditions for their cooperation, to develop comprehensive development strategies aimed at ensuring sustainable economic growth and support.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

Optimization of curdlan production and ultrasound assisted extraction processes from Priestia megaterium

In this study, the upstream and downstream production processes of curdlan from Priestia megaterium were optimized to enhance its yield. Additionally, a novel extraction method was developed for curdlan recovery. Optimization studies were conducted using Central composite design (CCD). Curdlan yield improved from 0.15 g/L (unoptimized) to 0.46 g/L (3-fold increase) when fermentation was carried out in CCD-optimized media of (w/v) sucrose 20%, urea 0.1%, KH2PO4 0.02%, agitation speed 250 rpm. To further enhance curdlan yield during extraction, ultrasonication was incorporated as a novel step into the conventional method of acid/alkali-assisted curdlan recovery. A two-step optimization was chosen for extraction, namely, one-factor-at-a-time (OFAT) and CCD, wherein the optimized extraction parameters were determined to be 25 s sonication, 1 N NaOH, and 2 h solubilization time. The curdlan yield improved by 1.5-fold (0.70 g/L) post optimization, in comparison with unoptimized conventional extraction step. Finally, the biopolymer was validated through characterization by nuclear magnetic resonance (NMR) which showed characteristic curdlan spectra in the13C and1H NMR studies. To the best of our knowledge, this study represents the first documented report on curdlan extraction using this novel method of ultrasonication.

End-To-End Automated Intact Protein Mass Spectrometry for High-Throughput Screening and Characterization of Bispecific and Multispecific Antibodies.

Bispecific antibodies (bsAbs) and multispecific antibodies (msAbs) represent a promising frontier in therapeutic antibody development, offering unique capabilities not achievable with traditional monoclonal antibodies. Despite their potential, significant challenges remain due to their increased molecular complexity. One prominent challenge is the correct assembly of light and heavy chains, as improper pairing leads to mispaired or incompletely assembled species that lack therapeutic efficacy and possess undesired properties, impairing the developability, manufacturability, and safety. There is a critical need for rapid, sensitive analytical tools to monitor and control these undesired species and ensure the quality assessment of bsAbs and msAbs. To address this need, we present a novel high-throughput, format-agnostic intact mass workflow that significantly enhances the efficiency of detecting and quantifying biotherapeutic products and related impurities. This workflow integrates automated sample preparation, novel high-resolution rapid mass detection powered by SampleStream-MS, and an advanced data analysis pipeline. It offers increased throughput and data quality while substantially reducing analysis turnover time and labor. This was demonstrated in a pilot program where ∼800 multispecific antibodies were processed in 10 working days. The article details the evaluation and validation of our method, demonstrating its repeatability and intermediate precision in terms of measurement accuracy and relative quantification of various product-related species. We underscore the transformative potential of this end-to-end high-throughput workflow in expediting bispecific and multispecific antibody discovery, optimizing production processes, and ensuring high-quality development and manufacturing for therapeutic antibodies.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

FORMATION OF STRATEGIC DIRECTIONS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE TO ACHIEVE THE GOALS OF SUSTAINABLE DEVELOPMENT

Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.

Optimization of Production Process of Alcohol Vinegar Macerated With Acai (Euterpe Precatoria) Using RSM to Add Colour and Functional Compounds to Vinegar

In Brazil, the alcohol vinegar is the cheapest vinegar found in the market and it has the lowest nutritional quality. The acai, a native Amazon fruit, is globally recognized as a super fruit and it is highly perishable. Research has been performed to deliver bioactive compounds and to substitute synthetic by the natural colourants in finished foods.

Research on the Development Status, Influencing Factors, and Countermeasures of Digital Transformation of China’s Manufacturing Enterprises

The manufacturing enterprises’ digital transformation is a complex and long-term process that requires the joint efforts of the government, enterprises, and society. By strengthening digital technology capacity building, promoting business model innovation, optimizing supply chain management, strengthening data sharing and circulation, strengthening policy support, and promoting industry-university-research cooperation, manufacturing enterprises can effectively adapt to the opportunities and challenges of the digital economy era and attain sustainable growth. This paper seeks to investigate the development status, key influencing factors, and corresponding countermeasures of manufacturing enterprise’s digital transformation. With the rapid growth of the digital economy, the manufacturing enterprise’s digital transformation has become a prominent way to enhance competitiveness, optimize production processes, and promote product innovation.

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop scheduling problem dominant in real-world printed circuit board assembly shops. A novel multi-objective hyper-heuristic combining Q-learning, i.e., two-stage improved spider monkey optimization (TS-ISMO), is tailored to address the complexities of the flow shop scheduling problems. The proposed method aims to simultaneously optimize conflicting objectives such as minimizing makespan, total energy consumption, and total tardiness time while incorporating lot streaming considerations. For multi-objective hyper-heuristic techniques, the algorithm dynamically selects and adapts a diverse set of low-level heuristics to explore the solution space comprehensively and strike a balance among competing objectives. The proposed TS-ISMO algorithm incorporates several significant features aimed at enhancing its performance. These features encompass hybrid heuristics for solution initialization, a contribution value method for comprehensive convergence and diversity assessment, diverse evolutionary state judgments to promote the algorithm’s balance between exploration and exploitation capabilities, and a Q-learning strategy for self-adaptive parameter tuning. The integration of Q-learning facilitates intelligent parameter control, enabling the algorithm to autonomously adjust its behavior based on past experiences and evolution dynamics. This adaptive mechanism enhances convergence speed and solution quality by effectively guiding the search process toward promising regions of the solution space. Extensive computational experiments are conducted on benchmark instances of hybrid flow shop scheduling problems with lot streaming to evaluate the performance of the proposed algorithm. Comparative analyses against state-of-the-art approaches demonstrate its superior solution quality and computational efficiency.

Capacity Constraint Analysis Using Object Detection for Smart Manufacturing

The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. Traditional industries facing capacity constraints require noninvasive methods for in-depth operations analysis to optimize processes and increase revenue. In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. This pipeline employs a Convolutional Neural Network (CNN)-based OD model to accurately identify potential objects on the production floor, followed by a CNN-based tracker to monitor their lifecycle in each workstation. The extracted metadata are further processed through the proposed framework. Our analysis of a real-world manufacturing facility over six months revealed that the bottleneck station operated at only 73.1% productivity, falling to less than 40% on certain days; additionally, the processing time of each item increased by 53% during certain weeks due to critical labor and materials shortages. These findings highlight significant opportunities for process optimization and efficiency improvements. The proposed pipeline can be extended to other production facilities where manual labor is used to assemble parts, and can be used to analyze and manage labor and materials over time as well as to conduct audits and improve overall yields, potentially transforming capacity management in smart manufacturing environments.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Investigation of Tensile Properties and In Situ Analysis of Fracture Behavior in High-Porosity Open-Cell Nickel Foam

Nickel foam offers excellent conductivity, a high surface area, and lightweight structure, making it ideal for applications, like battery electrodes, catalysts, and filtration systems. Its durability and corrosion resistance further enhance its performance in various industries. However, few studies focus on the tensile anisotropy of nickel foam and its tensile fracture process. In this study, the anisotropic tensile behavior of nickel foam with varying relative densities has been investigated, along with its tensile fracture behavior using in situ techniques. The tensile properties of nickel foams show strong anisotropy due to the flattening process in the production process. The results show that the tensile properties, including the yield strength, tensile strength, and elastic modulus, increase with the increasing relative density, while the elongation percentage has no relationship with the relative density. The experiment data on tensile strength are in agreement with Gibson’s formula and Liu’s formula. In situ tensile tests are conducted to explore the microscopic fracture mechanism of nickel foam. The results show that the struts of nickel foam are tensile fractures or shear fractures near the joints, and the fracture process of struts is clearly recorded and analyzed. This study is significant as it provides critical insights into the anisotropic tensile behavior of nickel foam and fracture mechanism, enabling the optimization of production processes and broadening its potential applications.

Effect of individual quick freezing treatment and cold storage on the host egg (Lepidoptera: Saturniidae) quality for the production of the parasitoid Anastatus japonicus Ashmead (Hymenoptera: Eupelmidae)

BackgroundThe augmentative release of the parasitoid species Anastatus japonicus Ashmead (Hymenoptera: Eupelmidae) is essential for managing the recent invasion of litchi stink bugs (Tessaratoma papillosa (Drury)) (Hemiptera: Tessaratomidae) in Taiwan. The mass release program in the spring targets this univoltine pest’s reproduction cycle. Low-temperature storage for the host eggs and parasitoids is required to maximize the production for the annual release program. This study aimed to enhance the shelf life of Samia cynthia (Lepidoptera: Saturniidae) eggs using the combination of refrigeration and individual quick freezing (IQF) as a storage treatment. Storage of A. japonicus in the IQF-treated S. cynthia eggs was evaluated to determine the optimal production process during the short preparation window before the release.ResultsThe influences of cold storage and IQF treatment on the host eggs were evaluated for different storage durations. The host quality was determined according to the emergence rate and female ratio of A. japonicus. The study’s results demonstrated that the host quality of S. cynthia eggs did not change significantly compared to the control group after 28 d of refrigeration at 7 °C. IQF treatment extended S. cyathia’s shelf life to 5 months without significantly affecting the host quality. Applying IQF at different developmental stages of the host eggs affected the parasitism rates of A. japonicus. Significantly higher emergence rates of A. japonicus were recorded in the eggs treated during the initial development (D0 to D2). However, storing parasitoids in the IQF-treated eggs under the current storage condition for one month at 12 °C significantly impacted the parasitoid’s emergence rate.ConclusionsThe results of this study highlight the potential benefits of the individual quick freezing technique. This technique didn’t alter the egg quality of S. cynthia, a crucial factor for the mass production of A. japonicus. The IQF treatment extends the shelf life of the host eggs to 5 months when stored at − 20 °C, a significant improvement compared to the 4-week shelf life without IQF treatment. This technology offers a promising and efficient approach for the mass production of A. japonicus and supports field releases. The treatment allowed for the stockpiling of host eggs during the off-season, which can help expedite the mass production of A. japonicus to fit an eight-week production window.

Comparison of desirability function (DF) and overall equipment effectiveness (OEE) with mathematical model for optimization in fruit juice production process

Comparison of desirability function (DF) and overall equipment effectiveness (OEE) with mathematical model for optimization in fruit juice production process

МҰНАЙ ЖӘНЕ ГАЗ КЕНОРЫНДАРЫНЫҢ ӨНІМДЕРІН ӨНДІРУДЕ ҚАЗІРГІ ЗАМАНҒЫ ЦИФРЛЫ ЖӘНЕ ЗИЯТКЕРЛІ ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНУ АСПЕКТІЛЕРІ

Most oil fields cover thousands of square kilometers, and some have reserves in difficult local conditions (heat, permafrost, desert, deep water) or difficult to recover. Due to these factors and a number of other problems related to technological safety, there are difficulties in the development of oil fields (with a steady increase in demand for oil and gas). Therefore, oil companies still need a highly reliable digital infrastructure that provides high productivity and security and manages many of the devices used in the oil and gas business throughout the field. Therefore, in recent years, oil companies around the world are increasingly managing their oil fields using "smart" technologies. They have proven to be a powerful tool for ensuring the safety of oil fields and increasing labor productivity, as well as economic efficiency. The technologies used include the improvement of all stages of the work process through the rapid acquisition of production data associated with advanced analytics, which contributes to a more complete and efficient development of the oil field. With experience in the implementation of "intelligent" technologies, it already shows high efficiency and very reliable results (both in terms of the oil production process itself and in terms of reducing the cost of individual operations). The use of modern digital and intelligent technologies in the production process in the oil and gas sector plays an important role in order to increase efficiency, ensure safety and reduce environmental impact. Application of digital technologies-data collection and analysis: IoT sensors (Internet of Things) allow you to collect data from deposits in real time and send it to analytical platforms. Field modeling: Using digital Twin technology, virtual field models are created that allow you to predict and optimize production processes. Automation: The automated systems and robotics used help to increase productivity in the management of the oil and gas production process.

Response surface optimization of a single-step castor oil-based biodiesel production process using a stator-rotor hydrodynamic cavitation reactor.

In order to combat environmental pollution and the depletion of non-renewable fuels, feasible, eco-friendly, and sustainable biodiesel production from non-edible oil crops must be augmented. This study is the first to intensify biodiesel production from castor oil using a self-manufactured cylindrical stator-rotor hydrodynamic cavitation reactor. In order to model and optimize the biodiesel yield, a response surface methodology based on a 1/2 fraction-three-level face center composite design of three levels and five experimental factors was used. The predicted ideal operating parameters were found to be 52.51°C, 1164.8rpm rotor speed, 27.43min, 8.4:1 methanol-to-oil molar ratio, and 0.89% KOH concentration. That yielded 95.51% biodiesel with a 99% fatty acid methyl ester content. It recorded a relatively low energy consumption and high cavitation yield of 6.09 × 105J and 12 × 10-3g/J, respectively. The generated biodiesel and bio-/petro-diesel blends had good fuel qualities that were on par with global norms and commercially available Egyptian petro-diesel. The preliminary cost analysis assured the feasibility of the applied process.