Process Industries Research Articles

Solving industrial fault diagnosis problems with quantum computers

In this article, we investigate in how far quantum computers can be leveraged to solve NP-complete fault diagnosis problems within the area of industrial cyber-physical systems. Therefore, two approaches are proposed which exploit quantum computing to solve diagnosis problems: The first method employs Grover’s algorithm, and the second is based on the Quantum Approximate Optimization Algorithm. To show the industrial application, we present an integrated approach to learn the diagnosis model from process data, check whether the model is suitable, and use it for diagnosis. The result is a method for quantum industrial fault diagnosis. For this approach, the diagnostic capabilities and the runtime have been evaluated on an IBM Falcon processor using three publicly available benchmarks from the process industry. Further, the scaling between quantum computers and classical PCs has been analyzed.

Risk analysis of urea manufacturing plant using fuzzy logic approach

The potential hazard element of chemical process industries has become severe with technological developments over the past few years. Fertilizer industries like the urea industry deal with highly toxic components in nature, causing severe consequences. This paper implements the fuzzy lambda-tau (λ-τ) approach with the Petri net (PN) model on a urea manufacturing plant. The PN model helps to reduce the complexity of the problem, whereas fuzzy logic helps to deal with the uncertainty and vagueness of data. The urea plant has been divided into three major units, that is, the reacting unit (RU), purifying unit (PU), and concentrating unit (CU). The different reliability parameters, such as failure rate, repair time, reliability, and mean time between failures (MTBF), are determined for all these units with different spread rates (±15%, ±25%, and ±35%). A significant change of around 75% and 9% was observed for overall repair time and MTBF, respectively, for a ±35% spread rate. The reliability parameters are found as a function of the square of the spread rate. These studies suggest that the PU of the urea plant is the most vulnerable one and thus requires timely maintenance to avoid any accidents in the future. Furthermore, a detailed qualitative study is done utilizing failure mode and effect analysis (FMEA) to outline the numerous failure causes in order to enhance the system's availability and maintainability. The limitations of conventional FMEA are addressed in this study by obtaining fuzzy RPN values after application of the fuzzy FMEA technique. On comparison of the RPN and FRPN values, the FRPN values are found to be more reliable while prioritizing the failure causes in process industries.

Kinetics study of pyrolysis of petroleum sludge and characterization of char

Thermochemical conversion (or pyrolysis) has emerged as an effective technique for the disposal of sludge generated in process industries. This paper reports physicochemical characterization and kinetic analysis of pyrolysis of sludge from a petroleum refinery and characterization of resultant char. The sludge had high volatile matter (>70 wt%) and low ash content (15 wt%). Pyrolysis of sludge was studied using a thermo-gravimetric analyzer (TGA). The TGA data was analyzed using two isoconversional models, viz. KAS and Vyazovkin AIC are used for the deduction of kinetic triplets, viz. activation energy, pre-exponential factor, and reaction mechanism. The activation energy (Ea ) of sludge pyrolysis varied in the 64.79–190.37 kJ/mol range. The pre-exponential factor varied from 1.18E + 07 to 4.29E + 12 s−1. The predominant solid-state mechanism of thermal conversion was an order-based reaction (F2/F3). The thermodynamic factors (viz. ΔH, ΔS, ΔG) of sludge pyrolysis were also determined. Relatively small values of (Ea – ΔH) ∼ 5 kJ/mol revealed high susceptibility of sludge for thermal conversion. Residual char after pyrolysis was characterized for physicochemical properties. The char properties were fixed carbon = 54.37%, calorific value = 22.48 MJ/kg, and BET surface area = 18.35 m2/g. These properties make char suitable for numerous applications.

Hyperspectral Imaging Combined with Machine Learning Can Be Used for Rapid and Non-Destructive Monitoring of Residual Nitrite in Emulsified Pork Sausages

The non-destructive and rapid monitoring system for residual nitrite content in processed meat products is critical for ensuring food safety and regulatory compliance. This study was performed to investigate the application of hyperspectral imaging in combination with machine learning algorithms to predict and monitor residual nitrite concentrations in emulsified pork sausages. The emulsified pork sausage was formulated with 1.5% (w/w) sodium chloride, 0.3% (w/w) sodium tripolyphosphate, 0.5% (w/w) ascorbic acid, and sodium nitrite at concentrations of 0, 30, 60, 90, 120, and 150 mg/kg, based on total sample weight. Hyperspectral imaging measurements were conducted by capturing images of the cross-sections and lateral sides of sausage samples in a linescan mode, covering the spectral range of 1000–2500 nm. The analysis revealed that higher nitrite concentrations could influence the protein matrix and hydrogen-bonding capacities, which might cause increased reflectance at approximately 1080 nm and 1280 nm. Machine learning models, including XGBoost, CATboost, and LightGBM, were employed to analyze the hyperspectral data. XGBoost demonstrated the best performance, achieving an R2 of 0.999 and a root mean squared error of 0.095, highlighting its high predictive accuracy. This integration of hyperspectral imaging with advanced machine learning algorithms offers a non-destructive and real-time method for monitoring residual nitrite content in processed meat products, noticeably improving quality control processes in the meat industry. Additionally, real-time implementation in industrial settings could further streamline quality control and enhance operational efficiency. Further research should focus on validating these findings with larger sample sizes and more diverse datasets to ensure robustness.

Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty

Renewable hydrogen production technology within the energy sector stands as a vital avenue towards decarbonization in process industries. However, for systems characterized by high penetration of renewable energy, the challenges posed by uncertain parameters impacting stable production present significant obstacles to the large-scale application of hydrogen production from renewable energy. To address the issues, this work proposed a comprehensive four-step strategy for the bottleneck identification and optimization of the renewable hydrogen production systems (RHPS), which encompasses four models including system configuration optimization, flexibility analysis, bottleneck identification, and debottlenecking models. The effectiveness of the proposed strategy is substantiated through a case study. The results indicate that positive fluctuations on supply side and negative fluctuations on demand side contribute to bolstering the flexibility of RHPS. When the RHPS possesses sufficient flexibility, the critical fluctuation amplitude for photovoltaic power is −36 %, whereas for wind turbine power, it stands at −32 %. It highlights that when the photovoltaic power fluctuates, the ability of RHPS to maintain its own stability is stronger than when the wind turbine power fluctuates. Furthermore, when the fluctuations of supply side are positive, the rate of change in critical fluctuation amplitudes on the supply side is 4.63 times than that of the demand side. This underscores that the power decline in the supply side has a more pronounced detrimental effect on the stability of RHPS than an increase in the demand side. The proposed strategy for optimizing RHPS offers valuable theoretical insights for the system design and retrofitting under uncertainty.

Solubility determination and thermodynamic modelling of verapamil hydrochloride in methanol-water binary solvent systems from 293.15 K to 323.15 K

Verapamil Hydrochloride is a key calcium channel blocker used to treat cardiovascular disorders and prevent cluster headaches by inhibiting calcium influx, thus improving cardiovascular health. The solubility of Verapamil Hydrochloride (VH) in methanol + water binary solvent systems were studied using a gravimetric method at temperatures from 293.15 K to 323.15 K under atmospheric pressure. The solubility increased with temperature and methanol content, with the highest mole fraction solubility at a 0.90 methanol mole fraction and the lowest at 0.10. Various models, including Apelblat, Van't Hoff, modified Jouyban–Acree, and CNIBS/R-K, were used to calculate theoretical solubility, with RAD and RMSD assessing the model accuracy. Thermodynamic analysis indicated that VH dissolution is endothermic and entropy-driven. This data is valuable for optimizing VH purification and crystallization processes in the pharmaceutical industry.

Process innovations and circular strategies for closing the water loop in a process industry

By implementing advanced wastewater treatment technologies coupled with digital tools, high-quality water is produced to be reused within the industry, enhancing process efficiency and closing loops. This paper investigates the impact of three innovation tools (process, circular and digital) in a Solvay chemical plant. Four technologies of the wastewater treatment plant “WAPEREUSE” were deployed, predicting their performance by process modelling and simulation in the PSM Tool. The environmental impact was assessed using Life Cycle Assessment and compared to the impact of the current industrial effluent discharge. The circularity level was assessed through three alternative closed-loop scenarios: (1) conventional treatment and discharge to sea (baseline), (2) conventional and advanced treatment by WAPEREUSE and discharge to sea, (3) conventional and advanced treatment by WAPEREUSE and industrial water reuse through cross-sectorial symbiotic network, where effluents are exchanged among the process industry, municipality and a water utility. Scenario 1 has the lowest pollutants’ removal efficiency with environmental footprint of 0.93 mPt/m3. WAPEREUSE technologies decreased COD by 98.3%, TOC by 91.4% and nitrates by 94.5%. Scenario 2 had environmental footprint of 1.12 mPt/m3. The cross-sectorial symbiotic network on the industrial value chain resulted in higher industrial circularity and sustainability level, avoiding effluents discharge. Scenario 3 is selected as the best option with 0.72 mPt per m3, reducing the environmental footprint by 21% and 36% compared to Scenarios 1 and 2, respectively.

Role of Emotional Intelligence on Employee Performance in Selected Public Sector Banks in Madhya Pradesh State (Jabalpur Region)

In the wake of a seemingly rigorous recruitment process in the banking industry, where candidates' intelligence level appears to be more important than their emotional state, this study investigated how employees' emotional intelligence is related with job performance in selected banks in Madhya Pradesh state. Emotional intelligence was categorized by empathy, while employee performance was categorized by employee turnover. This study used a correlational research design. Three banks from the state of Madhya Pradesh have been selected for coverage. Pearson's Product Moment Correlation Coefficient was used for data analysis. The results revealed that empathy has a strong negative relationship with employee turnover. It is recommended that management implement policies that both ensure the employment of emotionally intelligent employees and create opportunities within the organization that improve employee relations and employee performance to reduce employee turnover.

Deactivation mechanism of catalyst in long-period experiment of 1,1,2-TCE dehydrochlorination

ABSTRACT VDC, as an important raw material for high barrier material PVDC and lithium-ion battery binder PVDF, had an increasing demand. The catalytic dehydrochlorination of 1,1,2-TCE to generate VDC was a green process route that was expected to replace the heavily polluting sodium hydroxide saponification process in industry. There was a certain research foundation for the catalytic dehydrochlorination of 1,1,2-TCE, among which the core problem was that the catalyst was prone to deactivation. Previous studies had calculated the average molecular structure of the spent catalyst surface species when coconut shell activated carbon was used as the support. In subsequent studies, alumina support had been proven to be a better carrier for dehydrochlorination of 1,1,2-TCE, and the selected catalyst had been used in long-period laboratory experiments. The results indicated that the catalyst life had been significantly improved. In the early stage of the reaction, the activity of the catalyst decreased rapidly. In the first 20 h, the conversion of 1,1,2-TCE decreased from 49.35% to 40.36%. From 21 h to 120 h, the conversion of 1,1,2-TCE decreased from 39.96% to 36.18%, tending to stabilize. After 120 h of reaction, the catalyst was extracted and elemental analysis, FTIR, and GPC characterization were performed on the spent catalyst surface species. The average molecular formula of the carbon deposits was obtained as C96.08H50.29Cl36.53. Compared with coconut shell activated carbon support, under the action of activated alumina support, the route of catalyst carbon deposition had changed, and the sediment was more composed of polyvinylidene chloride components. Under certain conditions, sediment formed a dynamic equilibrium, which explained why the activity of the catalyst gradually stabilized. This study revealed the catalytic mechanism of active alumina supported catalyst and the pathway of carbon deposits generation, which was of great significance for improving the lifespan of improved catalyst.

Prediction of cavitation using machine learning techniques on centrifugal pump

Abstract When the local static pressure drops below the fluid’s vapour pressure at that particular temperature, a dynamic process known as cavitation takes place in the liquid. Due to cavitation, the performance (total head and flow rate) of the pump will deteriorate, and hence the vibration and noise will develop. Both the performance and cavitation investigations are carried out for a low-specific-speed pump as per the testing standards. The results obtained during cavitation studies are extracted to train the machine learning algorithms. The flow rate, speed, torque, suction head, and delivery head are considered as input variables for machine learning, with total head, Net Positive Suction Head (NPSH), efficiency, and noise magnitude being output variables. The current studies employ four machine learning techniques: random forest, support vector machines, extreme gradient boosting, and linear regression. In the analysis, notable performance was observed for the random forest and extreme gradient boosting algorithms among others consistently demonstrated superior predictive capabilities for the output parameters of the pumps. For linear regression, random forest, and extreme gradient boosting, the coefficient correlation values are around one for all output parameters except for noise. Similar observation is depicted for coefficient of determination and normalized root mean squared error. These ensure the level of noise during cavitation is very dynamic. These findings will enable the pump user to incorporate AI techniques for the diagnosis of centrifugal pump operation with reference to the nominal flow rate (best efficiency flow). Through this technology, preventive and predictive maintenance of pumps can be adapted with less downtime in process industries, power plants, chemical industries, oil and gas refineries, and so on.

Preface: 13th Ranshofener Leichtmetalltage

Abstract What strategies are available to minimize resource consumption in the light metal industry through recycling and energy efficiency? How can lightweight design make mobility more sustainable? And what role will artificial intelligence play in future processes in the metal processing industry? These and other questions are addressed at the 13th Ranshofener Leichtmetalltage 2024, which take place on the 26th–27th of September 2024 at the Hotel Gut Brandlhof in Saalfelden, Austria, and are organized in accordance with the criteria of the Austrian Ecolabel for Green Events. Under the guiding theme of “Light Metals Innovations for Environmental and Economic Sustainability”, this year’s conference offers an exciting program in three sessions “Digitalization in the Context of Circularity”, “Sustainable Process Development” and “Innovative Light Metals and their Characterization”. The focus is on decarbonization and digitalization in process and material development as well as the material characterization of light metals. A balanced spectrum of international presentations from universities, RTOs and industry provides participants with an up-to-date overview of the latest scientific findings and successful light metal applications. The 13th Ranshofener Leichtmetalltage 2024 are thus clearly focused on decarbonization and digitalization – even in times of diverse challenges. The first session, “Digitalization in the Context of Circularity”, highlights best-practice examples from the aluminium industry, the balance between sustainability and performance in metal processing and the implementation of complex geometries using wire-based additive manufacturing. The second session, “Sustainable Process Development”, is dedicated to the decarbonization and reduced material consumption of industrial processes, for example through GigaCasting, or the use of innovative recycling technologies. The third session, “Innovative Light Metals and their Characterization”, addresses forward-looking topics such as the development of special aluminium alloys for additive manufacturing, the use of old car scrap for innovative alloys and the decarbonization of the magnesium industry. The conference proceedings contain scientific contributions to the conference. We would like to thank all the authors for their high-quality work and the participants of the 13th Ranshofener Leichtmetalltage 2024 for their interest. We wish you stimulating reading and creative impulses! List of Scientific committee, Organizing committee are available in this Pdf.

Novel class of aldehyde reductases identified from Scheffersomyces stipitis for detoxification processes in cellulosic ethanol production industry

The increase in industrialization has led to a significant energy crisis, sparking interest in lignocellulosic biomass for fuel ethanol production because of its renewable characteristics. The complex composition of this biomass requires pretreatment to reduce inhibitors like furfural and hydroxymethylfurfural (HMF), which hinder enzymatic hydrolysis and fermentation, ultimately decreasing ethanol yields. This study investigates the detoxification mechanisms of furan aldehydes in Scheffersomyces stipitis, particularly through the upregulation of genes SsOYE2.2, SsOYE2.7, and SsOYE3.1 under furfural and HMF stress. Enzyme characterization determined that SsOye3.3p is the most active enzyme for reducing both compounds using NADPH. Notably, SsOye2.6p showed the highest catalytic efficiency towards furfural, while SsOye2.8p was optimal for HMF. The study also established the optimal temperature and pH for these enzymatic reactions. Importantly, SsOye2.5p displayed broad substrate specificity, indicating its potential in detoxifying various aldehydes in microbial cells. The findings suggest that genes linked to enhanced enzymatic properties were not significantly induced, indicating that S. stipitis has more substantial potential for furan aldehyde detoxification and can be developed as a chassis organism exhibiting furan aldehyde tolerance. These insights facilitate the development of novel enzymes to counter furan aldehyde inhibitors and the creation of furan aldehyde-tolerant strains via genetic engineering.

A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.

Developing a novel flavoured low alcohol beer using New Zealand honeydew honey and yacon concentrate

Demand for low alcohol beer (LAB) is growing around the world as consumer attitudes towards alcohol consumption shift, with health consciousness and sober driving listed among the possible drivers. In New Zealand, LAB is defined as having no more than 1.15% alcohol by volume (ABV). In industry, physical or biological processes are used to produce no- and low- alcohol beer; however, these methods are typically inaccessible to home brewers, and literature on accessible methods is non-existent. New Zealand honeydew honey (NZHDH) and New Zealand yacon concentrate (NZYC) have recently been chemically profiled in the literature. NZHDH and NZYC contain fermentable sugars, while NZYC also contains a very high percentage of fructooligosaccharides (FOS). This paper aims to incorporate these food materials into LABs using a method accessible to home brewers. The resultant LABs had an alcohol content between 0.61 and 0.86% ABV, well under the 1.15% threshold. Results from sensory analysis showed that all beer samples were somewhat acceptable to consumers; however, penalty analysis determined that hedonic scores could be improved by making the beers more sweet, bitter, and hoppy. Correspondence analysis revealed that the beers containing NZYC were more ‘foamy’ and ‘brown’ than all other beer samples, properties most likely related to the protein content and colour of NZYC respectively. Beers containing NZYC were also found to contain FOS and/or inulins, which are non-digestible sugars and therefore hypocaloric. This study provides an accessible method to produce LAB and shows how NZYC may be incorporated into LABs to yield a functional food.

Highly-efficient separation of pyromellitic acid and trimellitic acid mixtures via forming deep eutectic solvents: Experiment and calculation

Pyromellitic acid (PMA) and trimellitic acid (TMA) are significant chemical raw materials and intermediates. They simultaneously exist in the industry processes of synthesis and are difficult to be separated. In this work, several kinds of biodegradable compounds were chosen as hydrogen bond acceptors (HBAs) to separate PMA and TMA mixtures from acetone solutions via forming deep eutectic solvent (DES). It has been found that all these compounds can separate PMA and TMA mixtures to obtain pure PMA or TMA. However, the interaction between HBAs and PMA or TMA is quite different. Choline chloride cannot extract TMA but can form a DES with PMA in acetone. Hexamethylenetetramine (HA) and L-carnitine (L-car) can form DESs with both PMA and TMA in acetone solution. But when L-car or HA is added, the extraction rate of PMA is larger than that of TMA until the extraction rate of PMA reach 100%, and pure TMA is left in the acetone solution. The selective separation mechanism was explored by infrared spectroscopy combined with quantum chemistry calculation, and the strength and site of the interaction between extractants with PMA and TMA were calculated.

Optimization of geometrical parameters of positive guide vane for multi-stage pump as turbine based on orthogonal test

Abstract Multi-stage pumps as turbines (PAT) are commonly employed in the petrochemical, coal chemical, and other process industries to recover liquid residual pressure energy. This study focused on improving the performance of multi-stage PAT, specifically targeting the slope of the power-flow(P-Q) curve and hydraulic efficiency at the rated flow point. A two-stage PAT was selected as the research object for this study. According to the relationship between the power and the geometrical parameters of the positive guide vane derived based on the basic equation of the multi-stage PAT, four parameters of the positive guide vane were selected as the test factors for the orthogonal experimental study. A four-factor, three-level orthogonal table of type L9(34) was established. Using Fluent software and the RNG k-ε turbulence model to carry out numerical calculations of constant flow, the results showed that: for the slope of the P-Q curve, the influence of geometric parameter factors of the positive guide vane are in the order of the throat area, the outlet placement angle, the positive guide vanes number, and the base circle diameter; and for the hydraulic efficiency, the influence of geometric parameter factors of the positive guide vane are in the order of the throat area, the positive guide vanes number, the outlet placement angle, and the base circle diameter of the positive guide vane. At the rated flow point, the P-Q curve of the optimized solution decreased by 0.24, resulting in a flatter curve and a 0.3% increase in hydraulic efficiency. There was little change in the vicinity at the rated flow point. The best efficiency point of the optimized scheme is biased toward the high flow rate, the efficiency is increased by 1.85%, and the best efficiency zone is wider. Therefore, selecting appropriate positive guide vane geometry parameters can flatten the P-Q curve, improve internal flow, and enhance hydraulic efficiency.

Examining the role of virtual teams on project management processes in the Nigerian fintech sector

This study examined the role of virtual teams on project management processes in the Nigerian fintech industry. To achieve this aim, the study sought to examine how communication technology utilisation, trust, and geographical distribution of team members affected the project management processes of virtual team members in the Nigerian fintech industry. Building upon the theoretical frameworks of social presence theory and transaction cost theory, the study adopted a quantitative approach and used a sample of 385 employees of fintech firms in Nigeria. Using an electronic close-ended questionnaire (Google forms), data were collected from 378 participants, indicating a response rate of 98.2%. Purposive sampling technique was used to select the study’s participants. Data were analysed using both descriptive (frequencies and percentages) and inferential statistics (multiple linear regression) via the aid of STATA 17. Findings from the study revealed that communication technology utilization (β = 0.3114694, p < 0.05) and geographical distribution of team members (β =0.7622249, p < 0.05) both had statistically significant effects on project management processes. Trust was found to have an insignificant effect on project management processes (β = -0.0085031, p > 0.798). The study concluded that virtual teams significantly and positively affects project management processes in the Nigerian fintech industry, and recommends that firms in the industry should continue with the use of virtual teams in their given projects. Also, the study recommends that policies regarding work in the virtual environment should be formalised so as to maximise the benefits associated with virtual work.

Experimental and theoretical insights into the influence of gamma irradiation degradation products of N,N,N′,N′-tetraoctyl diglycolamide on extraction efficiency toward trivalent f-elements

Understanding the effect of radiation rays on N,N,N′,N′-tetraoctyl diglycolamide (TODGA) based solvent extraction systems is an essential task for establishing and optimizing related extraction processes in nuclear industries. However, the influence of various radiolysis degradation products on extraction efficiency and the mechanism involved still remain elusive. In this work, the radiolysis degradation products of TODGA were analyzed with the aid of high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) technique and documented results, followed by the successful synthesis of ten radiolysis degradation products P1–P10. The extraction ability of each main degradation product towards Eu(III) and their effects on the extraction performance of TODGA system were systematically studied. These radiolysis degradation products with diverse chemical structures were found to reveal different roles in extraction such as serving as co-extractant and synergist, in which P2 resemblance in structure to TODGA offers the highest extraction ability among others, thus mitigating the decrease of extraction efficiency. Mechanism investigations including slope analysis and density functional theory (DFT) calculations reveal that P2 is involved as one of the extracted species in the complexation with Eu(III) in its first coordination sphere. These findings provide in-depth experimental and theoretical guidance on the molecular scale for designing TODGA-based metal separation processes.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Overview of Key Technologies in Surgical Robots and Optimization Discussions

Surgical robot is the national strategic diagnostic and therapeutic equipment research focus, get a number of scientific research institutes, colleges and universities and enterprises pay extensive attention to the design and development of a variety of surgical robots, and registration declaration. This article explores the critical technologies of surgical robots and key areas for optimizing their performance, including kinematic positioning errors, pose errors, feedback model errors, image recognition positioning errors, path planning, and safety aspects. The findings not only provide a scientific basis for future standardization research on surgical robots but also offer significant theoretical and practical references for the research, manufacturing, and registration processes in the medical robotics industry.