Records with Subject: System Identification
Showing records 1 to 25 of 38. [First] Page: 1 2 Last
Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method
Qilan Ran, Yedong Song, Wenli Du, Wei Du, Xin Peng
October 30, 2022 (v1)
Keywords: canonical correlation analysis, data-driven, diesel engine, Fault Detection, variable selection
In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and im... [more]
Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae
Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Agoston E. Eiben, Stanley Brul
January 24, 2022 (v1)
Keywords: derivative-free optimization, dynamic models, evolutionary computing, glycolysis, metabolism, yeast
One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neigh... [more]
A 2-stage Approach to Parameter Estimation of Differential Equations using Neural ODEs
William Bradley, Fani Boukouvala
November 7, 2021 (v1)
Keywords: Neural ODEs, Neural-Networks, Nonlinear programming, parameter estimation
Modeling physio-chemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation intensive and uncertain due to the presence of nonlinearity, noise, and sparsity in the data, which in turn causes convergence to local minima and divergence issues. This work proposes a merger of Machine Learning (ML) and mechanistic approaches by employing ML models as a means to fit nonlinear mechanistic ODEs. Using a two-stage indirect approach, Neural ODEs are used to estimate state derivatives, which are then used to estimate the parameters of a more interpretable mechanistic ODE model. In addition to its computational efficiency, the proposed method demonstrates the ability of Neural ODEs to better estimate derivative information than interpolating methods based on algebraic... [more]
Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms
Nikesh Patel, Brandon Corbett, Johan Trygg, Chris McCready, Prashant Mhaskar
July 29, 2021 (v1)
Keywords: data driven model identification, missing data, subspace identification
This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.
A Robust Method for the Estimation of Kinetic Parameters for Systems Including Slow and Rapid Reactions—From Differential-Algebraic Model to Differential Model
Tapio Salmi, Esko Tirronen, Johan Wärnå, Jyri-Pekka Mikkola, Dmitry Murzin, Valerie Eta
June 21, 2021 (v1)
Keywords: dimethyl carbonate, kinetics, robust parameter estimation, slow and rapid reactions
Reliable estimation of kinetic parameters in chemical systems comprising both slow and rapid reaction steps and rapidly reacting intermediate species is a difficult differential-algebraic problem. Consequently, any conventional approach easily leads to serious convergence and stability problems during the parameter estimation. A robust method is proposed to surmount this dilemma: the system of ordinary differential equations and nonlinear algebraic equations is converted to ordinary differential equations, which are solved in-situ during the parameter estimation. The approach was illustrated with two generic examples and an example from green chemistry: synthesis of dimethyl carbonate from carbon dioxide and methanol.
Hot Melt Extrusion Processing Parameters Optimization
Abdullah Alshetaili, Saad M. Alshahrani, Bjad K. Almutairy, Michael A. Repka
June 10, 2021 (v1)
Keywords: design of experiment, experimental trials, hot-melt extrusion, process parameters
The aim of this study was to demonstrate the impact of processing parameters of the hot-melt extrusion (HME) on the pharmaceutical formulation properties. Carbamazepine (CBZ) was selected as a model water-insoluble drug. It was incorporated into Soluplus®, which was used as the polymeric carrier, to produce a solid dispersion model system. The following HME-independent parameters were investigated at different levels: extrusion temperature, screw speed and screw configuration. Design of experiment (DOE) concept was applied to find the most significant factor with minimum numbers of experimental runs. A full two-level factorial design was applied to assess the main effects, parameter interactions and total error. The extrudates’ CBZ content and the in vitro dissolution rate were selected as response variables. Material properties, including melting point, glass transition, and thermal stability, and polymorphs changes were used to set the processing range. In addition, the extruder torq... [more]
Computer-Aided Nonlinear Frequency Response Method for Investigating the Dynamics of Chemical Engineering Systems
Luka A. Živković, Tanja Vidaković-Koch, Menka Petkovska
May 11, 2021 (v1)
Keywords: experimental identification, frequency response functions, nonlinear process dynamics, periodic processes, Process Intensification, process systems engineering
The Nonlinear Frequency Response (NFR) method is a useful Process Systems Engineering tool for developing experimental techniques and periodic processes that exploit the system nonlinearity. The basic and most time-consuming step of the NFR method is the derivation of frequency response functions (FRFs). The computer-aided Nonlinear Frequency Response (cNFR) method, presented in this work, uses a software application for automatic derivation of the FRFs, thus making the NFR analysis much simpler, even for systems with complex dynamics. The cNFR application uses an Excel user-friendly interface for defining the model equations and variables, and MATLAB code which performs analytical derivations. As a result, the cNFR application generates MATLAB files containing the derived FRFs in a symbolic and algebraic vector form. In this paper, the software is explained in detail and illustrated through: (1) analysis of periodic operation of an isothermal continuous stirred-tank reactor with a sim... [more]
An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli
Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Muhammad Akmal Remli, Noorlin Mohd Ali, Kohbalan Moorthy, Noryanti Muhammad
December 22, 2020 (v1)
Keywords: kinetic model, kinetic parameters estimation, metabolic engineering, PSO algorithm, Se-PSO algorithm
Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The... [more]
Water Cycle Algorithm for Modelling of Fermentation Processes
Olympia Roeva, Maria Angelova, Dafina Zoteva, Tania Pencheva
December 17, 2020 (v1)
Keywords: fed-batch fermentation processes, Genetic Algorithm, parameter identification, water cycle algorithm
The water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly a... [more]
Model Calibration of Stochastic Process and Computer Experiment for MVO Analysis of Multi-Low-Frequency Electromagnetic Data
Muhammad Naeim Mohd Aris, Hanita Daud, Khairul Arifin Mohd Noh, Sarat Chandra Dass
July 17, 2020 (v1)
Keywords: computer experiment, computer simulation, CST software, EM data, Gaussian process, MVO analysis, stochastic process
An electromagnetic (EM) technique is employed in seabed logging (SBL) to detect offshore hydrocarbon-saturated reservoirs. In risk analysis for hydrocarbon exploration, computer simulation for subsurface modelling is a crucial task. It can be expensive and time-consuming due to its complicated mathematical equations, and only a few realizations of input-output pairs can be generated after a very lengthy computational time. Understanding the unknown functions without any uncertainty measurement could be very challenging as well. We proposed model calibration between a stochastic process and computer experiment for magnitude versus offset (MVO) analysis. Two-dimensional (2D) Gaussian process (GP) models were developed for low-frequencies of 0.0625−0.5 Hz at different hydrocarbon depths to estimate EM responses at untried observations with less time consumption. The calculated error measurements revealed that the estimates were well-matched with the computer simulation technology (CST) ou... [more]
Distinct and Quantitative Validation for Predictive Process Modelling in Steam Distillation of Caraway Fruits and Lavender Flower Following a Quality-By-Design (QbD) Approach
Thorsten Roth, Lukas Uhlenbrock, Jochen Strube
July 17, 2020 (v1)
Keywords: caraway, Carum carvi, essential oil, Lavandula, lavender, Modelling, physico-chemical model, steam distillation
A quality by design (QbD) approach as part of process development in the regulated, pharmaceutical industry requires many experiments. Due to the large number, process development is time consuming and cost intensive. A key to modern process development to reduce the number of required experiments is a predictive simulation with a validated physico-chemical model. In order to expand the process expertise of steam distillation through maximum information, a model development workflow is used in this paper, which focuses on implementation, verification, parametrization and validation of a physico-chemical model. Process robustness and sensitivity of target values can be determined from the developed general model and design of experiments with statistical evaluations. The model validation is exemplified by two different types of plant systems, caraway fruits (Carum Carvi) and lavender flowers (Lavandula).
An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares
Ming Yang, Zirui Liu, Jiang Long, Wanying Qu, Dianguo Xu
June 23, 2020 (v1)
Keywords: full-order observer, motor control, parameter identification
In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment resu... [more]
Response Surface Methodology as a Useful Tool for Evaluation of the Recovery of the Fluoroquinolones from Plasma—The Study on Applicability of Box-Behnken Design, Central Composite Design and Doehlert Design
Andrzej Czyrski, Hubert Jarzębski
June 23, 2020 (v1)
Keywords: drug analysis, fluoroquinolones, Optimization, recovery
The aim of this study was to find the best design that is suitable for optimizing the recovery of the representatives of the 2nd, 3rd and 4th generation of fluoroquinolones. The following designs were applied: Central Composite Design, Box−Behnken Design and Doehlert Design. The recovery, which was a dependent variable, was estimated for liquid−liquid extraction. The time of shaking, pH, and the volume of the extracting agent (dichloromethane) were the independent variables. All results underwent the statistical analysis (ANOVA), which indicated Central Composite Design as the best model for evaluation of the recovery. For each analyte, an equation was generated that enabled to estimate the theoretical value for the applied conditions. The graphs for these equations were provided by the Response Surface Methodology. The statistical analysis also estimated the most significant factors that have an impact on the liquid−liquid extraction, which occurred to be pH for ciprofloxacin and moxi... [more]
Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho−Kalman Method for Order Estimation
Alexios Papacharalampopoulos
June 10, 2020 (v1)
Keywords: digital twin, manufacturing process, system identification, system order
System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.
Fast Screening Methods for the Analysis of Topical Drug Products
Margarida Miranda, Catarina Cardoso, Carla Vitorino
June 10, 2020 (v1)
Keywords: RP-HPLC, semi-solid dosage forms, topical products, validation
Considering the recent regulatory requirements, the overall importance of in vitro release testing (IVRT) methods regarding topical product development is undeniable, especially when addressing particulate systems. For each IVRT study, several hundreds of samples are generated. Therefore, developing rapid reversed-phase high-performance liquid chromatography (RP-HPLC) methods, able to provide a real-time drug analysis of IVRT samples, is a priority. In this study, eight topical complex drug products exhibiting distinct physicochemical profiles were considered. RP-HPLC methods were developed and fully validated. Chromatographic separations were achieved on a XBridgeTM C18 (5 µm particle size, 150 mm × 2.1 mm), or alternatively on a LiChrospher® 100 RP-18 (5 µm particle size, 125 mm × 4.6 mm) at 30 °C, under isocratic conditions using UV detection at specific wavelengths. According to the physicochemical characteristics of each drug, different mobile phases were selected. Irrespective of... [more]
Data-Driven Modelling of the Complex Interaction between Flocculant Properties and Floc Size and Structure
Anita Lourenço, Marco S. Reis, Julien Arnold, Maria Graca Rasteiro
May 22, 2020 (v1)
Keywords: flocculation, laser diffraction spectroscopy, polyelectrolytes, statistical modelling, wastewater treatment
Polymeric flocculants are widely used due to their ability to efficiently promote flocculation at low dosages. However, fundamental background knowledge about how they act and interact with the substrates is often scarce, or insufficient to infer the best chemical configuration for treating a specific effluent. Inductive, data-driven approaches offer a viable solution, enabling the development of effective solutions for each type of effluent, overcoming the knowledge gap. In this work, we present such an inductive workflow that combines the statistical design of experiments and predictive modelling, and demonstrates its effectiveness in the development of anionic polymeric flocculants for the treatment of a real effluent from the potato crisps manufacturing industry. Based on the results presented, it is possible to conclude that the hydrodynamic diameter, charged fraction and concentration are the parameters with a stronger influence on the characteristics of flocs obtained when using... [more]
Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction
Moritz Schulze, René Schenkendorf
April 1, 2020 (v1)
Keywords: differential flatness, model selection, model-based design of experiments, nonlinear programming, parameter uncertainty, point estimate method
Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification of model candidates from a set of various model hypotheses. To identify the best experimental design suitable for a reliable model selection and system identification is challenging for nonlinear (bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness for model selection problems under uncertainty, and thus translates the model selection problem to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal controls for improved model selection trajectories are expressed analytically with low computational costs. We further demo... [more]
An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems
Nguyen Gia Minh Thao, Kenko Uchida
February 24, 2020 (v1)
Keywords: automatic-tuning scheme, boundary points, interval fuzzy modeling, linear programming, lower bound, min-max optimization, photovoltaic/wind/battery power system., upper bound
This paper proposes an improved interval fuzzy modeling (imIFML) technique based on modified linear programming and actual boundary points of data. The imIFML technique comprises four design stages. The first stage is based on conventional interval fuzzy modeling (coIFML) with first-order model and linear programming. The second stage defines reference lower and upper bounds of data using MATLAB. The third stage initially adjusts scaling parameters in the modified linear programming. The last stage automatically fine-tunes parameters in the modified linear programming to realize the best possible model. Lower and upper bounds approximated by the imIFML technique are closely fitted to the reference lower and upper bounds, respectively. The proposed imIFML is thus significantly less conservative in cases of large variation in data, while robustness is inherited from the coIFML. Design flowcharts, equations, and sample MATLAB code are presented for reference in future experiments. Perform... [more]
Optimal Design of Experiments for Liquid−Liquid Equilibria Characterization via Semidefinite Programming
Belmiro P.M. Duarte, Anthony C. Atkinson, José F.O. Granjo, Nuno M.C. Oliveira
December 13, 2019 (v1)
Keywords: approximate designs, liquid–liquid equilibria, optimal design of experiments, semidefinite programming, ternary systems
Liquid−liquid equilibria (LLE) characterization is a task requiring considerable work and appreciable financial resources. Notable savings in time and effort can be achieved when the experimental plans use the methods of the optimal design of experiments that maximize the information obtained. To achieve this goal, a systematic optimization formulation based on Semidefinite Programming is proposed for finding optimal experimental designs for LLE studies carried out at constant pressure and temperature. The non-random two-liquid (NRTL) model is employed to represent species equilibria in both phases. This model, combined with mass balance relationships, provides a means of computing the sensitivities of the measurements to the parameters. To design the experiment, these sensitivities are calculated for a grid of candidate experiments in which initial mixture compositions are varied. The optimal design is found by maximizing criteria based on the Fisher Information Matrix (FIM). Three op... [more]
Mechanism and Kinetics of Ammonium Sulfate Roasting of Boron-Bearing Iron Tailings for Enhanced Metal Extraction
Xiaoshu Lv, Fuhui Cui, Zhiqiang Ning, Michael L. Free, Yuchun Zhai
December 13, 2019 (v1)
Keywords: ammonium sulfate roasting process, iron tailings, kinetics, reaction mechanism
The large amount of boron-bearing iron tailings in China is a resource for metals that needs to be more completely and efficiently utilized. In this evaluation, the ammonium sulfate roasting process was used to make a controllable phase transformation to facilitate the subsequent extraction of valuable metals from boron-bearing iron tailings. The effects of roasting temperature, roasting time, the molar ratio of ammonium sulfate to tailings, and the particle size on the extraction of elements were investigated. The orthogonal experimental design of experiments was used to determine the optimal processing conditions. XRD (X-Ray Diffractomer), scanning electron microscope (SEM), and simultaneous DSC−TG analyzer were used to assist in elucidating the mechanism of ammonium sulfate roasting. The experimental results showed that nearly all Fe, Al, and Mg were extracted under the following conditions: (1) the molar ratio of ammonium sulfate to iron tailings was 3:1; (2) the roasting temperatu... [more]
Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm
Wen-Jing Shen, Han-Xiong Li
December 10, 2019 (v1)
Keywords: Levenberg-Marquardt (LM) algorithm, lithium-ion battery (LIB), multi-scale parameter identification, particle swarm optimization (PSO)
This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD) of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the iden... [more]
Activation Energy Determination in Case of Independent Complex Kinetic Processes
Giorgio Luciano, Roman Svoboda
December 10, 2019 (v1)
Keywords: activation energy, complex processes, solid-state kinetics
Theoretically simulated kinetic data were used to evaluate the performance of the most common isoconversional methods of kinetic analysis in complex-process scenarios with two independent overlapping processes exhibiting nucleation-growth kinetics, and further expand the conclusions for the autocatalytic kinetic processes with positive asymmetry. In close-to-real-life situations all the integral isoconversional methods provided practically indistinguishable E-α outcomes. The Friedman and incremental modified Vyazovkin methods results in significant over- and undershoots. However, the combined utilization of the integral and differential isoconversional methods was demonstrated to greatly contribute to the interpretation of the E-α dependences and estimation of E1 and E2—the conceptual evaluation involving positions of inflection points and plateaus is introduced. The influence of the range of applied heating rates q+ on the course of E-α dependences was studied. In this regard, the per... [more]
Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay
Haitham Osman
December 10, 2019 (v1)
Keywords: ANOVA statistical test, lube oil treatment, process optimization, response surface method, UV-VIS spectrophotometer
In this work, used lube oil was treated using natural acid-free clay. Clay was added at different amounts (5, 10, and 20 g) to 100 mL of waste engine oil at various temperatures (250, 350, 400, and 450 °C) and mixed at a speed of 800 rpm for 30 min. After settling and separation, the treated oil was diluted with kerosene before being examined using a Ultraviolet−visible (UV) spectrophotometer. In order to achieve cost-effective recycling, this process is modeled using the response surface method (RSM). Five regression models (linear, quadratic, Two Factor Interactions (2FI), cubic, and reduced-order quadratic model) were developed, then tested, and examined by calculating the statistical performance indicators (R2, R2adj, Akaike’s Information Criterion corrected (AICc), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE)). The results obtained reveal that the modified quadratic model outperforms the rest of the models in terms of the low value of RMSE, the lowest AI... [more]
Performance Optimization of High Specific Speed Centrifugal Pump Based on Orthogonal Experiment Design Method
Zikang Li, Hongchang Ding, Xiao Shen, Yongming Jiang
December 10, 2019 (v1)
Keywords: high specific speed centrifugal pump, orthogonal design method, performance optimization
A high specific speed centrifugal pump is used in the situation of large flow and low head. Centrifugal pump parameters need to be optimized in order to raise its head and efficiency under off-design conditions. In this study, the orthogonal experiment design method is adopted to optimize the performance of centrifugal pump basing on three parameters, namely, blade outlet width b2, blade outlet angle β2 and blade wrap angle φ. First, the three-dimensional model of the centrifugal pump is established by CFturbo and SolidWorks. Then nine different schemes are designed by using orthogonal table, and numerical simulation is carried out in CFX15.0. The final optimized combination of parameters is b2 = 24 mm, β2 = 24°, φ = 112°. Under the design condition, the head and efficiency of the optimized centrifugal pump are appropriately improved, the increments of which are 0.74 m and 0.48%, respectively. However, the efficiency considerably increases at high flow rates, with an increase of 6.9% a... [more]
Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network
Musa Yahaya Pudza, Zurina Zainal Abidin, Suraya Abdul Rashid, Faizah Md Yasin, Ahmad Shukri Muhammad Noor, Mohammed A. Issa
December 9, 2019 (v1)
Keywords: artificial neural network, carbon dots, hydrothermal, organic, photoluminescence, response surface methodology, tapioca
Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg−Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer... [more]
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