Process Parameters For Production Research Articles

Evolution of transition metal dichalcogenide film properties during chemical vapor deposition: from monolayer islands to nanowalls

Unique properties possessed by transition metal dichalcogenides (TMDs) attract much attention in terms of investigation of their formation and dependence of their characteristics on the production process parameters. Here, we investigate the formation of TMD films during chemical vapor deposition (CVD) in a mixture of thermally activated gaseous H2S and vaporized transition metals. Our observations of changes in morphology, Raman spectra, and photoluminescence (PL) properties in combination with in situ measurements of the electrical conductivity of the deposits formed at various precursor concentrations and CVD durations are evidence of existence of particular stages in the TMD material formation. Gradual transformation of PL spectra from trion to exciton type is detected for different stages of the material formation. The obtained results and proposed methods provide tailoring of TMD film characteristics necessary for particular applications like photodetectors, photocatalysts, and gas sensors.

Multi-physics coupled numerical simulation study to optimize process parameters for electromagnetic stirring of semi-solid A356 aluminum alloy under the influence of skin effect

The electromagnetic stirring (EMS) method stands as a widely used technique in creating semi-solid aluminum alloy slurry due to its convenience and precision in parameter control compared to other methods. In this study, a comprehensive model that combines electromagnetics, flow, heat transfer and solidification was formed. By analyzing the influence of input current and frequency on the electromagnetic force, the behavior of various process parameters in velocity field and temperature field under influence of the skin effect is investigated. The aim is to find the optimal process parameters for production of slurry using the low-frequency EMS. The results show that within the investigated range, by adjusting the input current and frequency to 150 A and 15 Hz, higher fluid velocities and appropriate distribution of vortex zones can be observed, while achieving a uniform temperature field, the highest cooling rate and the minimum radial temperature difference. In addition, the sample prepared under the optimal process parameters shows that the melt edge is least affected by the skin effect, which leads to a higher stirring strength and a smaller overall grain size, which is consistent with the theoretical skin effect research found within the simulation section.

Experimental and Theoretical Investigation of Thermal Parameters Influencing the Freezing Process of Ice Cream

Freezing time stands out as the most critical parameter in ice cream production, significantly influencing the final product’s quality and production efficiency. Therefore, it is essential to accurately estimate the freezing time based on ice cream recipes. This research paper focuses on determining the thermal properties of ice cream samples by leveraging insights from previous studies. Moreover, a new specific heat correlation is proposed to account for the latent heat effect of water during the freezing process. The results demonstrated that the new specific heat correlation aligns well with established formulas from previous research as well as experimental results with maximum 2.5% deviation. To validate the accuracy of the proposed numerical model, experimental studies were compared with numerical results for the first time in the context of ice cream freezing inside a mold. Additionally, a parametric analysis was conducted using numerical modelling to discern the relative importance of various production process parameters. Notably, the glycol–water mixture temperature emerged as the most influential parameter affecting freezing time, while the amount of air inside the ice cream was identified as the least significant factor.

Recycling of Waste PET Plastic on Asphalt, by Using Application of Response Surface Methodology: Effect of Production Process Parameters

Plastic, polymers of variable compositions have become object of common use and difficult to digest by micro-organisms, especially single-use plastic waste, such as polyethylene terephthalate (PET) water canisters. These polymers are source of environmental pollution. Therefore, it is important to manage theme in the good way to protect environment. In this study, different conditions of waste plastic PET (Polyethylene Terephthalate) recycling on asphalt were optimized. Response Surface Methodology (RSM) using the Doehlert experimental design has been employed in the optimization. The independent variables considered were bitumen (5-8%), PET (0-12%), Mixing temperature (150-160°C) and Mixing time (20-30min). Four-second order polynomial models were generated. The responses obtained by the models were well described as: specific density (Y.<SUB>SD</SUB>), penetrability (Y.<SUB>P</SUB>), softening point (Y.<SUB>SP</SUB>), and flash point (Y.<SUB>FP</SUB>) of the process with satisfactory fits in terms of absolute average deviation, bias factor and accuracy factor. The optimum responses were 1,04 as specificity gravity (Y.<SUB>SG</SUB>), 60*(1/10mm) as penetrability at 25°C, 100g and 5sec (Y.<SUB>P</SUB>), 50°C as softening point (Y.<SUB>SP</SUB>), and 242°C as flash point (Y.<SUB>FP</SUB>). The statistical relation between the four independent variables and the process responses were well described.

Recycling of Waste PET Plastic on Asphalt, by Using Application of Response Surface Methodology: Effect of Production Process Parameters

Plastic, polymers of variable compositions have become object of common use and difficult to digest by micro-organisms, especially single-use plastic waste, such as polyethylene terephthalate (PET) water canisters. These polymers are source of environmental pollution. Therefore, it is important to manage theme in the good way to protect environment. In this study, different conditions of waste plastic PET (Polyethylene Terephthalate) recycling on asphalt were optimized. Response Surface Methodology (RSM) using the Doehlert experimental design has been employed in the optimization. The independent variables considered were bitumen (5-8%), PET (0-12%), Mixing temperature (150-160°C) and Mixing time (20-30min). Four-second order polynomial models were generated. The responses obtained by the models were well described as: specific density (Y.<SUB>SD</SUB>), penetrability (Y.<SUB>P</SUB>), softening point (Y.<SUB>SP</SUB>), and flash point (Y.<SUB>FP</SUB>) of the process with satisfactory fits in terms of absolute average deviation, bias factor and accuracy factor. The optimum responses were 1,04 as specificity gravity (Y.<SUB>SG</SUB>), 60*(1/10mm) as penetrability at 25°C, 100g and 5sec (Y.<SUB>P</SUB>), 50°C as softening point (Y.<SUB>SP</SUB>), and 242°C as flash point (Y.<SUB>FP</SUB>). The statistical relation between the four independent variables and the process responses were well described.

Effects of Ultrafine and Homogenization Process on Microstructure and Properties of H13 Die Steel

Abstract The mixed grain structure was found in H13 die steel due to improper production process parameters, and mixed grain structure has adverse effects on the performance of H13 die steel. The appearance of mixed grain structure is analysed. The heredity of mixed grain structure in H13 die steel was eliminated by using ultrafine and homogenization processes. The characteristics of the microstructure of H13 die steel were investigated, and mechanical properties were tested. The results indicated that the mixed grain structure transformed into fine equiaxed grains after the ultrafine and homogenization process and the average grain size of austenite is about 20.7 μm. After quenching and tempering treatment, the hardness of the sample reached 47.1 HRC, and the impact absorption energy was 10.8 J.

Ultra-early prediction of the process parameters of coal chemical production

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

Characterization of Interfacial Corrosion Behavior of Hybrid Laminate EN AW-6082 ∪ CFRP.

The corrosion behavior of a hybrid laminate consisting of laser-structured aluminum EN AW-6082 ∪ carbon fiber-reinforced polymer was investigated. Specimens were corroded in aqueous NaCl electrolyte (0.1 mol/L) over a period of up to 31 days and characterized continuously by means of scanning electron and light microscopy, supplemented by energy dispersive X-ray spectroscopy. Comparative linear sweep voltammetry was employed on the first and seventh day of the corrosion experiment. The influence of different laser morphologies and production process parameters on corrosion behavior was compared. The corrosion reaction mainly arises from the aluminum component and shows distinct differences in long-term corrosion morphology between pure EN AW-6082 and the hybrid laminate. Compared to short-term investigations, a strong influence of galvanic corrosion on the interface is assumed. No distinct influences of different laser structuring and process parameters on the corrosion behavior were detected. Weight measurements suggest a continuous loss of mass attributed to the detachment of corrosion products.

Optimization of formulation and processing conditions for the production of functional noodles containing orange‐fleshed sweet potatoes and biofortified beans

AbstractThe global demand for noodles continues to increase due to their convenience, wide appeal, and affordability. Instant noodles, in particular, are popular for their easy preparation. With annual consumption reaching 106 billion servings in 2019, there is a growing awareness of the importance of healthy food options. However, most noodle types currently available commercially are of low nutritional value. This study sought to develop a protocol for the production of functional noodles consisting of orange‐fleshed sweet potatoes (OFSP) puree and biofortified bean powder (BBP). Response surface methodology (RSM) was used to optimize product formulation and processing parameters. Reduced quartic models were found to adequately represent the relationship between dependent variables (hardness, moisture, protein, dietary fiber, iron, and zinc content) and independent variables (dough thickness, drying temperature, and drying time). R2 values were 0.86–0.99, with a nonsignificant lack‐of‐fit (p < .05). Using numerical optimization, the optimal protocol for the production of functional noodles was determined to include formulation consisting of wheat 73%, OFSP 21.5%, and BBP 5.5%; dough thickness of 2.0 mm; drying temperature and time of 80.0°C and 143.4 min, respectively. These conditions yielded noodles with 5.9% moisture, 11.0 N hardness, 34.5% protein, 11.9% dietary fiber, 86.9 ppm (parts per million) iron, and 50.53 ppm zinc, with a desirability value of 0.82. Experimental validation demonstrated no significant difference from predicted values. Sensory evaluation rated the noodles as acceptable to consumers, with an overall acceptability of 7.8 on a 9‐point hedonic scale. These results show the potential of OFSP and BBP as ingredients for acceptable and nutrient‐rich noodles.

LIQUID NEURAL NETWORKS: PRINCIPLE OF WORK AND AREAS OF APPLICATION

The article deals with the architecture of liquid neural networks (LNN) and their potential in modern technologies. Thanks to the constant development of algorithms and hardware, neural networks are becoming more and more powerful and efficient, which opens up new opportunities for their application. The authors describe the principle of operation of liquid neural networks, which includes the process of learning and inference, which allows effective use of the natural dynamics of the system to solve various tasks, including classification, prediction, and control. We note that the concept of LNNs arose as an attempt to overcome some of the limitations and problems faced by traditional neural networks. The study considers the basic concepts and principles of LNNs and their application potential in various fields, from robotics to medicine and industry. The study also determines the main advantages and disadvantages of LNNs compared to traditional models. It is possible to use them to process a large stream of data, such as video, audio, or sensory data from various sensor types, allowing robots to receive information about their environment and make decisions based on that data. In medical diagnostics and image processing, liquid neural networks can significantly contribute to the quality and efficiency of diagnostic procedures. LNNs can enable the implementation of automatic control systems that monitor and regulate parameters of production processes or adapt to changes in the environment and optimise parameters to achieve maximum productivity and product quality. The field of LNN lacks standards and is limited to using performance metrics. Establishing standards and objective metrics will allow researchers and engineers to understand and compare different LNN implementations. Although LNNs are relatively efficient in terms of power consumption, their implementation at the hardware level may require new technologies and architectures to optimise performance. As a result, the study outlines the prospects for the further development of this technology. Keywords: liquid neural networks, artificial intelligence, adaptive control, learning efficiency, application potential.

Legume Fortified Instant Noodles: Optimization of Product Formulation and Processing Parameters using Response Surface Methodology

Cereal-pulses-based fortification has been attempted to improve the protein and dietary fibre content of instant noodles without altering the overall acceptability. Response surface methodology (RSM) was employed to optimize the process parameters and level of ingredients for manufacturing instant noodles. The effect of maize flour and split yellow pea flour fortification (6-18% each), the addition of vital wheat gluten (0.4-2.0%), and steaming time (1.5-7.5 min) on cooking loss, water uptake, volume expansion, cooking time, overall acceptability, L*, a* and b* values were investigated using the central composite rotatable design (CCRD). Results showed that maize flour and split yellow pea flour significantly (p≤0.05) affected cooking loss, water uptake, volume expansion, cooking time, overall acceptability, L*, a*, and b* values at linear levels. Vital wheat gluten had a significant effect on cooking loss, overall acceptability, L*, and a* value. Steaming time had a significant interactive effect with split yellow pea flour and vital wheat gluten on the responses. Incorporation of 18.0% maize flour, 11.25% split yellow pea flour, and 0.4% vital wheat gluten in formulation with 5.87 minutes of steaming time resulted in optimum quality instant noodles. A 20% increase in protein and a 50% increase in fiber content was observed in optimized instant noodles.

Process Investigation on Robust Electrospinning of Non-Aligned and Aligned Polyvinylidene Fluoride Nanofiber Mats for Flexible Piezoelectric Sensors.

Polyvinylidene fluoride (PVDF) nanofiber mats have played a significant role in wearable electronic devices that have been in great demand in recent decades. Although manifold PVDFbased freely stacked or well-aligned nanofiber mats created via the electrospinning process have been demonstrated to achieve multisensory capabilities with high sensitivity and long detection range, rarely have any of them proved their ability with a stable process and accurate processing parameters. In this work, we successfully developed freely stacked and well-aligned PVDF nanofiber mats with diameters ranging from micrometers to nanometers, providing stable performance for wearable electronic devices. Through in-depth investigations into material preparation, electrospinning, and fiber collection processes, we revealed the relationship between the nanofiber morphology, β-phase fraction, and piezoelectric output with various process parameters. Characterized by analytical methods, we have established a mature, reliable nanofiber mat fabrication system capable of mass-producing PVDF nanofibers with the required diameter and consistent properties. At 18 kV voltage and 60% RH humidity, the uniformity of the fiber diameter and β-phase content was maintained in a favorable range. When the drum speed increased to 2000 r/s, the fiber orientation and β-phase content increased. We assembled aligned PVDF nanofiber mats with conductive fabric in a flexible piezoelectric sensor that successfully monitored different body movements and produced an output voltage of 0.1 V. This study provides the necessary process parameters for the large-scale production of high-quality PVDF nanofiber mats and provides clear guidance for beginners in the field of nanofiber mat manufacturing.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Analysis of the Uniformity of Mechanical Properties along the Length of Wire Rod Designed for Further Cold Plastic Working Processes for Selected Parameters of Thermoplastic Processing.

This study presents the results of research, the aim of which was to analyze the uniformity of the distribution of selected mechanical properties along the length of a 5.5 mm diameter wire rod of 20MnB4 steel for specific thermoplastic processing parameters. The scope of the study included, inter alia, metallographic analyses, microhardness tests, thermovision investigations, and tests of the wire rod mechanical properties (yield strength, ultimate tensile strength, elongation, relative reduction in area at fracture), along with their statistical analysis, for three technological variants of the rolling process differing by rolling temperature in the final stage of the rolling process (Reducing Sizing Mill rolling block [RSM]) and by cooling rate using STELMOR® cooling process. The obtained results led to the conclusion that the analyzed rolling process is characterized by a certain disparity of the analyzed mechanical properties along the length of the wire rod, which, however, retains a certain stability. This disparateness is caused by a number of factors. One of them, which ultimately determines the properties of the finished wire rod, is the process of controlled cooling in the STELMOR® line. Despite technological advances concerning technical solutions (among them, increasing the roller track speed in particular sections), it is currently not possible to completely eliminate the temperature difference along the length of the wire rod caused by the contact of individual coils with each other. From this point of view, for the analyzed thermoplastic processing parameters, there is no significant impact by the production process parameters on the quality of the finished steel product. Whereas, while comparing the mechanical properties and microstructure of the wire rod produced in the different technological combinations, it was found that the wire rod rolled in an RSM block at 850 °C and cooled after the rolling process on a roller conveyor at 10 °C/s had the best set of mechanical properties and the smallest microstructure variations. The wire rod produced in this way had the required level of plasticity reserve, which enables further deformation of the given type of steel in compression tests with a relative plastic strain of 75%. The uniformity of mechanical properties along the length of wire rods designed for further cold plastic working processes is an important problem. This is an important issue, given that wire rods made from 20MnB4 steel are an input material for further cold plastic working processes, e.g., for the drawing processes or the production of nails.

Optimization of Pyrolysis Process Parameters for Fuel Oil Production from the Thermal Recycling of Waste Polypropylene Grocery Bags Using the Box–Behnken Design

The impact of dumping plastic waste is realized in different ecosystems of the planet. Several methods have been adopted to dispose of these wastes for energy recovery. This study, for the first time, proposed the Box–Behnken design technique to optimize the pyrolysis process parameters for fuel oil production from waste polypropylene (PP) grocery bags using a semibatch-type pyrolytic reactor. The semibatch-type pyrolytic reactor was developed and employed to produce fuel oil from waste PP grocery bags. The effect of different process parameters on fuel oil production was comprehensively analyzed using the response surface methodology (RSM) with the conjunction of the Box–Behnken design (BBD). The BBD facilitates the prediction of the response variables with respect to changes in the input variables by developing a response model. The BBD was used to optimize the process parameters, such as the reaction temperature (400–550 °C), nitrogen flow rate (5–20 mL min−1), and substrate feed rate (0.25–1.5 kg h−1), and their effect on the responses were observed. The optimum response yields of the fuel oil (89.34 %), solid residue (2.74%), and gas yield (7.92%) were obtained with an optimized temperature (481 °C), a nitrogen flow rate (13 mL min−1), and a feed rate (0.61 kg h−1). The quadratic model obtained for the fuel oil response denotes the greater R2 value (0.99). The specific gravity and calorific value of the fuel oil were found to be 0.787 and 45.42 MJ kg−1, respectively. The fuel oil had higher research octane number (RON) (100.0 min) and motor octane number (MON) (85.1 min) values. These characteristics of the fuel oil were matched with conventional petroleum fuels. Further, Fourier transform infrared spectroscopy (FT-IR) and gas chromatography–mass spectroscopy (GC-MS) were used to analyze the fuel oil, and the results revealed that the fuel oil was enriched with different hydrocarbons, namely, alkane (paraffins) and alkene (olefins), in the carbon range of C4–C20. These results, and also the fractional distillation of the fuel oil, show the presence of petroleum-range hydrocarbons in the waste PP fuel oil.

Nutrient Properties of Fresh and Composted Cotton Gin Byproducts and Cattle Manure for Soil Amendment

Highlights N and P concentrations in composted vs. fresh Cotton Gin Byproducts (CGB) were at least 55% (dry basis) higher. P concentrations in composted vs. raw Beef Manure (BM) were approximately 25% higher. Characterization informs product formulations and process parameters for co-pelleting CGB with BM. Accurate formulation would enhance the potential for sustainable utilization of both byproducts. Abstract. Applications of fresh and composted Cotton Gin Byproducts (CGB) and cattle manure as soil amendments are very common practices in the industry. However, composting and direct application of the materials are often limited by factors such as bulkiness, varying nutrient composition and application rates, and handling/transportation costs. This study was conducted to characterize fresh and composted CGB and beef manure (BM) for subsequent co-pelletization and utilization as soil amendments, and to investigate the effects of composting on the nutrient composition and agronomic values. Raw and composted samples of CGB and BM at different storage periods and composting ages were sourced from two commercial gins and a beef farm. Nutrient content, acidity, and compost maturity indices such as carbon/nitrogen (C/N) and ammonium/nitrate (NH4-N/NO3-N) ratios were determined and compared. All samples were obtained at four sampling points on the respective storage or composting piles. The results showed that composted CGB generally contained at least 55% more total-N and phosphorus oxide (P2O5), but approximately 35% less potassium oxide (K2O) than fresh CGB. Composted BM had approximately 3% less total-N, but at least 25% more P2O5 than raw BM. The nutrient compositions affirm the potential for co-pelletizing both forms of CGB and BM to improve the agronomic values and enhance the utilization as soil amendments. In addition, establishing the various properties of CGB and BM is crucial for determining product formulations and selecting process parameters for co-pelletization. Keywords: Beef manure, Composting, Cotton ginning, Nutrient composition, Soil fertility.

OPTIMIZATION OF PROCESS PARAMETER OF BIODIESEL DERIVED FROM A HYBRID BLEND OF KARANJA AND CHLORELLA VULGARIS OIL

In this study, the response surface approach was employed to predict and optimize the biodiesel yield. Blend of Karanja and Chlorella vulgaris oil was used in a volumetric ratio of 75:25 to lower the free fatty acid content. The biodiesel production process parameters from this hybrid oil were optimized through a single-step transesterification process. The optimized conditions resulted in a maximum biodiesel yield of 97.55% . These conditions included a catalyst amount of 1.09% (w/w), a reaction time of 91.47 minutes, a reaction temperature of 56.86°C, and a methanol-to-oil molar ratio of 8.46:1. The experimental biodiesel yield was compared with the predicted response obtained from the response surface methodology (RSM). The comparison demonstrated good agreement between the experimental and predicted values.

Взгляд на функционирование проектной организации через призму теории СМО

In the context of the ongoing digitalization of the Russian economy as a whole and the construction industry in particular, solutions aimed at increasing the efficiency of construction organizations that carry out their functions at various stages of the construction project lifecycle remain in demand. Recognizing that the primary task of any organizations or enterprise in the construction industry, including project organizations, is the efficient using of all types of resources, the set objective can be achieved by reducing the organization’s overheads while at the same time increasing the efficiency of decisions aimed at managing core production processes. It is important to view the entire production process as a unified system with the ability to evaluate its performance indicators and forecast changes in these indicators when production process parameters will be changed.One potential modeling approach for the development of design and estimate documentation processes may involve conceiving it as a queuing system. From the authors’ perspective, in terms of queuing theory the activities of a project team under the guidance of the Chief project engineer (CPE) can be represented as a multi-phase open queuing network. The article provides computation formulas for the determining several key performances indicators of queuing system operation, which can be used to evaluate the performances of both individual performers of parts of the design and estimate documentation as well as the entire project team. Furthermore, this approach offers the possibility of calculating the effectiveness of changing these indicators with minimal expenses for the organization.

High-purity corundum grinding balls, obtained by quasi-isostatic pressing

The influence of the composition of press powders based on alumina CT 3000 LS SG, quasi-isostatic pressing and firing parameters on the structural and mechanical characteristics of grinding balls with a diameter of 40 mm is shown. Process parameters of production of high-density corundum balls are optimized on the basis of revealed regularities. The results of complex studies of physical and mechanical properties of ceramic articles are given. The determining effect of the particle size of raw material component on the hardness, density and operational characteristics of the grinding bodies was revealed.

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.