Records with Subject: System Identification
Showing records 1 to 25 of 555. [First] Page: 1 2 3 4 5 Last
Integrating Hybrid Modeling and Multifidelity Approaches for Data-Driven Process Model Discovery
Suryateja Ravutla, Fani Boukouvala
July 9, 2024 (v1)
Keywords: Data-driven modeling, Hybrid modeling, Model identification, Multifidelity, Sparse regression
Modeling the non-linear dynamics of a system from measurement data accurately is an open challenge. Over the past few years, various tools such as SINDy and DySMHO have emerged as approaches to distill dynamics from data. However, challenges persist in accurately capturing dynamics of a system especially when the physical knowledge about the system is unknown. A promising solution is to use a hybrid paradigm, that combines mechanistic and black-box models to leverage their respective strengths. In this study, we combine a hybrid modeling paradigm with sparse regression, to develop and identify models simultaneously. Two methods are explored, considering varying complexities, data quality, and availability and by comparing different case studies. In the first approach, we integrate SINDy-discovered models with neural ODE structures, to model unknown physics. In the second approach, we employ Multifidelity Surrogate Models (MFSMs) to construct composite models comprised of SINDy-discover... [more]
Improving Mechanistic Model Accuracy with Machine Learning Informed Physics
William Farlessyost, Shweta Singh
July 9, 2024 (v1)
Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while mainta... [more]
Design Space Identification of the Rotary Tablet Press
Mohammad Shahab, Sunidhi Bachawala, Marcial Gonzalez, Gintaras Reklaitis, Zoltan Nagy
July 9, 2024 (v1)
Keywords: design space, direct compression, Optimization, pharmaceutical process, tablet press
The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidan... [more]
Development of Mass/Energy Constrained Sparse Bayesian Surrogate Models from Noisy Data
Samuel Adeyemo, Debangsu Bhattacharyya
July 9, 2024 (v1)
This paper presents an algorithm for developing sparse surrogate models that satisfy mass/energy conservation even when the training data are noisy and violate the conservation laws. In the first step, we employ the Bayesian Identification of Dynamic Sparse Algebraic Model (BIDSAM) algorithm proposed in our previous work to obtain a set of hierarchically ranked sparse models which approximate system behaviors with linear combinations of a set of well-defined basis functions. Although the model building algorithm was shown to be robust to noisy data, conservation laws may not be satisfied by the surrogate models. In this work we propose an algorithm that augments a data reconciliation step with the BIDSAM model for satisfaction of conservation laws. This method relies only on known boundary conditions and hence is generic for any chemical system. Two case studies are considered-one focused on mass conservation and another on energy conservation. Results show that models with minimum bia... [more]
Research on an Intelligent Identification Method for Wind Turbine Blade Damage Based on CBAM-BiFPN-YOLOV8
Hang Yu, Jianguo Wang, Yaxiong Han, Bin Fan, Chao Zhang
June 21, 2024 (v1)
Keywords: attention mechanism, feature fusion, loss function, wind turbine blade, YOLOv8
To address challenges in the detection of wind turbine blade damage images, characterized by complex backgrounds and multiscale feature distribution, we propose a method based on an enhanced YOLOV8 model. Our approach focuses on three key aspects: First, we enhance the extraction of small target features by integrating the CBAM attention mechanism into the backbone network. Second, the feature fusion process is refined using the Weighted Bidirectional Feature Pyramid Network (BiFPN) to replace the path aggregation network (PANet). This modification prioritizes small target features within the deep features and facilitates the fusion of multiscale features. Lastly, we improve the loss function from CIoU to EIoU, enhancing sensitivity to small targets and the perturbation resistance of bounding boxes, thereby reducing the gap between computed predictions and real values. Experimental results demonstrate that compared with the YOLOV8 model, the CBAM-BiFPN-YOLOV8 model exhibits improvement... [more]
UnA-Mix: Rethinking Image Mixtures for Unsupervised Person Re-Identification
Jingjing Liu, Haiming Sun, Wanquan Liu, Aiying Guo, Jianhua Zhang
June 21, 2024 (v1)
Keywords: generalization, mixup, person re-identification, unsupervised learning
With the development of ultra-long-range visual sensors, the application of unsupervised person re-identification algorithms to them has become increasingly important. However, these algorithms inevitably generate noisy pseudo-labels, which seriously hinder the performance of tasks over a large range. Mixup, a data enhancement technique, has been validated in supervised learning for its generalization to noisy labels. Based on this observation, to our knowledge, this study is the first to explore the impact of the mixup technique on unsupervised person re-identification, which is a downstream task of contrastive learning, in detail. Specifically, mixup was applied in different locations (at the pixel level and feature level) in an unsupervised person re-identification framework to explore its influences on task performance. In addition, based on the richness of the information contained in the person samples to be mixed, we propose an uncertainty-aware mixup (UnA-Mix) method, which red... [more]
A Study on Defect Detection of Dissimilar Joints in Cu-STS Tubes Using Infrared Thermal Imaging of Induction Heating Brazing
Chung-Woo Lee, Suseong Woo, Jisun Kim
June 21, 2024 (v1)
Keywords: brazing, convolutional neural network, defect identification, high-frequency Induction heating, infrared thermal image
We proposed a novel detection method for identifying joint defects in the brazing process between copper tubes and stainless steel using a convolutional neural network (CNN) model. The brazing joints were created using high-frequency induction heating equipment, and infrared thermal imaging cameras were employed to capture the thermal data generated during the jointing process. The experiments involved 15.88 mm diameter copper tubes commonly used in plate heat exchangers, stainless-steel tubes, and filler metal containing 20% Ag. The thermal data were obtained with a resolution of 80 × 80 pixels per frame, resulting in 4796 normal joint data and 5437 defective joint data collected over 100 high-frequency induction-heating brazing experiments. A total of 10,233 thermal imaging data were categorized into 6548 training data, 1638 validation data, and 2047 test data for the development of the predictive model. We designed CNN models with varying hyperparameters, specifically the number of... [more]
Precise Lightning Strike Detection in Overhead Lines Using KL-VMD and PE-SGMD Innovations
Xinsheng Dong, Jucheng Liu, Shan He, Lu Han, Zhongkai Dong, Minbo Cai
June 7, 2024 (v1)
Keywords: KL-VMD, lightning strike, PE-SGMD, traveling wave, zero-mode voltage
When overhead lines are impacted by lightning, the traveling wave of the fault contains a wealth of fault information. The accurate extraction of feature quantities from transient components and their classification are fundamental to the identification of lightning faults. The extraction process may involve modal aliasing, optimal wavelet base issues, and inconsistencies between the lightning strike distance and the fault point. These factors have the potential to impact the effectiveness of recognition. This paper presents a method for identifying lightning strike faults by utilizing Kullback−Leibler (KL) divergence enhanced Variational Mode Decomposition (VMD) and Symmetric Geometry Mode Decomposition (SGMD) improved with Permutation Entropy (PE) to address the aforementioned issues. A model of a 220 kV overhead line is constructed using real faults to replicate scenarios of winding strike, counterstrike, and short circuit. The three-phase voltage is chosen and then subjected to Kar... [more]
Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin
Junkai Chen, Xili Deng, Xin Shan, Ziyan Feng, Lei Zhao, Xianghua Zong, Cheng Feng
June 7, 2024 (v1)
Keywords: extreme gradient boosting, honey badger optimization algorithm, Hongche fault zone, lithology identification, volcanic rock
Lithology identification is the fundamental work of oil and gas reservoir exploration and reservoir evaluation. The lithology of volcanic reservoirs is complex and changeable, the longitudinal lithology changes a great deal, and the log response characteristics are similar. The traditional lithology identification methods face difficulties. Therefore, it is necessary to use machine learning methods to deeply explore the corresponding relationship between the conventional log curve and lithology in order to establish a lithology identification model. In order to accurately identify the dominant lithology of volcanic rock, this paper takes the Carboniferous intermediate basic volcanic reservoir in the Hongche fault zone as the research object. Firstly, the Synthetic Minority Over-Sampling Technique−Edited Nearest Neighbours (SMOTEENN) algorithm is used to solve the problem of the uneven data-scale distribution of different dominant lithologies in the data set. Then, based on the extreme... [more]
Modeling of Quantitative Characterization Parameters and Identification of Fluid Properties in Tight Sandstone Reservoirs of the Ordos Basin
Bo Xu, Zhenhua Wang, Ting Song, Shuxia Zhang, Jiao Peng, Tong Wang, Yatong Chen
June 7, 2024 (v1)
Keywords: fluid property identification, logging interpretation, model, siliciclastic reservoirs, tight sandstone, unconventional petroleum resources
The Ordos Basin has abundant resources in its tight sandstone reservoirs, and the use of well logging technology stands out as a critical element in the exploration and development of these reservoirs. Unlike conventional reservoirs, the commonly used interpretation models are not ideal for evaluating tight sandstone reservoirs through logging. In order to accurately evaluate parameters and identify fluid properties in the tight sandstone reservoirs of the Ordos Basin, we propose the adaption of conventional logging curves. This involves establishing an interpretation model that integrates the response characteristics of logging curves to tight sandstone reservoirs in accordance with the principles of logging. In this approach, we create interpretation models specifically for shale content, porosity, permeability, and saturation within the tight sandstone reservoir. Using the characteristics of the logging curves and their responses, we apply a mathematical relationship to link these p... [more]
Development of an Experimental Dead-End Microfiltration Layout and Process Repeatability Analysis
Gorazd Bombek, Luka Kevorkijan, Grega Hrovat, Drago Kuzman, Aleks Kapun, Jure Ravnik, Matjaž Hriberšek, Aleš Hribernik
June 7, 2024 (v1)
Keywords: filtration, parameter, pressure oscillations, process, repeatability
Microfiltration is an important process in the pharmaceutical industry. Filter selection and validation is a time-consuming and expensive process. Quality by design approach is important for product safety. The article covers the instrumentalization and process control of a laboratory-scale dead-end microfiltration layout. The layout is a downscale model of the actual production line, and the goal is filter validation and analysis of process parameters, which may influence filter operation. Filter size, fluid pressure, valve plunger speed, and timing issues were considered. The focus is on the identification of the most influential process parameters and their influence on the repeatability of pressure oscillations caused by valve opening. The goal was to find the worst-case scenario regarding pressure oscillations and, consequently, filter energy intake. The layout was designed as compact as possible to reduce pressure losses between the filter and valve. Valve-induced pressure oscill... [more]
A Multimodal Fusion System for Object Identification in Point Clouds with Density and Coverage Differences
Daniel Fernando Quintero Bernal, John Kern, Claudio Urrea
June 7, 2024 (v1)
Keywords: density differences, LiDAR, multimodal fusion, object identification, point clouds, point coverage
Data fusion, which involves integrating information from multiple sources to achieve a specific objective, is an essential area of contemporary scientific research. This article presents a multimodal fusion system for object identification in point clouds in a controlled environment. Several stages were implemented, including downsampling and denoising techniques, to prepare the data before fusion. Two denoising approaches were tested and compared: one based on neighborhood technique and the other using a median filter for each “x”, “y”, and “z” coordinate of each point. The downsampling techniques included Random, Grid Average, and Nonuniform Grid Sample. To achieve precise alignment of sensor data in a common coordinate system, registration techniques such as Iterative Closest Point (ICP), Coherent Point Drift (CPD), and Normal Distribution Transform (NDT) were employed. Despite facing limitations, variations in density, and differences in coverage among the point clouds generated by... [more]
An Improved On-Line Recursive Subspace Identification Method Based on Principal Component Analysis and Sliding Window for Polymerization
Jiayu Qian, Jubin Zhang, Ting Lei, Silin Li, Chen Sun, Guanghua He, Bin Wen
June 7, 2024 (v1)
Keywords: polymerization, principal component analysis, sliding window, subspace identification
Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introdu... [more]
Exploration of Temperature Inversion in Intermediate Joints of 10 kV Three-Core Cable
Xinhai Li, Qizhong Chan, Yue Ma, Jiangjun Ruan, Aogang Hou
June 6, 2024 (v1)
Keywords: hot spot temperature, inversion identification, temperature field distribution, three-core cable
In order to precisely ascertain the temperature at the hot spot within the intermediate joint of a three-core cable, this study focused on a 10 kV three-core cable joint as its primary subject. A three-dimensional finite element model of the cable joint was constructed, enabling the calculation of both the steady-state hot spot temperature field distribution and the transient temperature rise curve of the joint. Employing a one-dimensional transient thermal path model for the cable body, a radial inversion model for the cable core temperature was established. Through simulating the transient temperature field of the cable joint under varying currents, a fitting relationship was determined for the axial temperature points of the cable core. Subsequently, an inversion perception model was devised to calculate the hot spot temperature of the cable joint based on temperature measurements at specific points on the outer surface of the cable. Under both continuous and periodic loads, the inv... [more]
Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study
Qizhen Li, Gangwei Fan, Dongsheng Zhang, Wei Yu, Shizhong Zhang, Zhanglei Fan, Yue Fu
June 6, 2024 (v1)
Keywords: decision tree, discriminant function equation, mine water inrush source, mixing degree of water sources
After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ < 39.585 mg/L, Cl− < 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ < 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed wate... [more]
New Method for Monitoring and Early Warning of Fracturing Construction
Jiani Hu, Meilong Fu, Yang Yu, Minxuan Li
June 5, 2024 (v1)
Keywords: monitoring and early warning, sand-blocking prediction, sand-plugging warning index method
During fracturing operations, special situations are often encountered. For example, the insufficient proppant-carrying capacity of fracturing fluid can cause quartz sand or ceramsite to settle near the wellbore and form a sand plug. Alternatively, excessive sand injection intensity can lead to severe accumulation of injected sand near the wellbore and also form a sand plug. These special situations are reflected in the fracturing operation curve as an abnormal increase in oil pressure over a short period of time. If not handled promptly, they can have unimaginable consequences. Sand plugs in fracturing operations, characterized by their speed and unpredictability, often form rapidly, within about 20 s. Conventional methods for on-site sand-plug warnings during fracturing include the oil pressure−time double logarithmic slope method and the net pressure−time double logarithmic slope method. Although these methods respond quickly, their warning results are unstable and vary significantl... [more]
MALDI-TOF Mass Spectrometry-Based Identification of Aerobic Mesophilic Bacteria in Raw Unpreserved and Preserved Milk
Nataša Mikulec, Jasminka Špoljarić, Dijana Plavljanić, Nina Lovrić, Fabijan Oštarić, Jasenka Gajdoš Kljusurić, Khan Mohd. Sarim, Nevijo Zdolec, Snježana Kazazić
June 5, 2024 (v1)
Keywords: aerobic mesophilic bacteria, MALDI-TOF mass spectrometry, preserved milk, raw unpreserved milk, sodium azide (NaN3)
The number of aerobic mesophilic bacteria in milk is one of the indicators of the hygienic quality of milk. The aim of this work was to determine such aerobic mesophilic bacteria and their number in raw unpreserved milk and milk preserved with sodium azide. In 40 collected samples, the total number of aerobic mesophilic bacteria was determined using the classical method of counting colonies on a nutrient medium according to the international standard HRN EN ISO 4833-1:2013. The results showed a trend of decreasing the number of grown colonies in milk preserved with sodium azide. MALDI-TOF mass spectrometry also successfully identified 392 bacterial colonies in raw unpreserved milk samples and 330 colonies in preserved milk samples. Of these, 30 genera and 54 bacterial species were identified in the raw unpreserved milk samples, while 27 genera and 41 bacterial species were identified in the preserved samples. By using a collective approach, the present study provided a more detailed in... [more]
Anomaly Identification for Photovoltaic Power Stations Using a Dual Classification System and Gramian Angular Field Visualization
Zihan Wang, Qiushi Cui, Zhuowei Gong, Lixian Shi, Jie Gao, Jiayong Zhong
June 5, 2024 (v1)
Keywords: anomaly detection, attention matrix, CNN, Gramian angular field, PV power station, time series data
With the increasing scale of photovoltaic (PV) power stations, timely anomaly detection through analyzing the PV output power curve is crucial. However, overlooking the impact of external factors on the expected power output would lead to inaccurate identification of PV station anomalies. This study focuses on the discrepancy between measured and expected PV power generation values, using a dual classification system. The system leverages two-dimensional Gramian angular field (GAF) data and curve features extracted from one-dimensional time series, along with attention weights from a CNN network. This approach effectively classifies anomalies, including normal operation, aging pollution, and arc faults, achieving an overall classification accuracy of 95.83%.
A Robust Process Identification Method under Deterministic Disturbance
Youngjin Yook, Syng Chul Chu, Chang Gyu Im, Su Whan Sung, Kyung Hwan Ryu
June 5, 2024 (v1)
Keywords: deterministic disturbance, disturbance modeling, integral transform, Laguerre polynomials, process identification
This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic distur... [more]
Microseismic Monitoring of the Fracture Nucleation Mechanism and Early Warning for Cavern Rock Masses
Jin-Shuai Zhao, Yue-Mao Zhao, Peng-Xiang Li, Chong-Feng Chen, Jian-Cong Zhang, Jiang-Hao Chen
February 19, 2024 (v1)
Keywords: early warning, fracture nucleation, microseismic monitoring, stability analysis, underground cavern
The rock mass is susceptible to instability and damage during cavern construction. The blast-induced cracking process of the rock mass contains a wealth of information about the precursors of instability, and the identification of fracture nucleation signals is a prerequisite for effective hazard warning. A laboratory mechanical test and microseismic (MS) monitoring were carried out in the Baihetan Cavern to investigate the fracture nucleation process in the rock mass. MS monitoring shows that pre-existing microcracks were closed or new cracks were generated under the action of high stress, which caused the migration of microcracks. As the crack density increases, the fracture interaction gradually increases. The study of the rock fracture nucleation mechanism helps to reveal the MS sequences during the rock fracture process, and the fore-main shock was found in the MS sequence during access tunnel excavation. This study can effectively provide guidance for the early warning of rock ma... [more]
Dynamic Modeling and Parameter Identification of Double Casing Joints for Aircraft Fuel Pipelines
Lingxiao Quan, Chen Fu, Renyi Yao, Changhong Guo
February 10, 2024 (v1)
Keywords: double casing joint, flow–solid coupling, free modal, parameters identification
Double casing joints are flexible pipe joints used for connecting aircraft fuel pipelines, which can compensate for the displacement and corner of the connected pipes and have complex mechanical characteristics. However, it is difficult to use sensors to directly measure the mechanical connection parameters of flexible joints. In this paper, we construct a coupling dynamics model and parameter identification of a double casing joint. Firstly, we analyze the structure and working principle of double-layer casing joints and establish the dynamics model of a single-layer flexible joint based on the transfer matrix method. Then, we deduce the coupling matrix of the inner and outer pipeline according to the deformation coordination conditions combined with matrix dimension extension. We establish the coupling dynamics model of flow−solid coupling of double casing joints. Furthermore, parameters such as equivalent stiffness and damping of each motion of the double casing joint in the casing... [more]
LC-MS/MS and GC-MS Identification of Metabolites from the Selected Herbs and Spices, Their Antioxidant, Anti-Diabetic Potential, and Chemometric Analysis
Hafiza Sehrish Kiani, Baber Ali, Mohammad Khalid Al-Sadoon, Hamad S. Al-Otaibi, Akhtar Ali
February 10, 2024 (v1)
Keywords: antioxidants, diabetes, drug discovery, flavonoids, herbs, human health, phytochemicals, spices, volatile compounds
Culinary herbs and spices are widely used in daily diets. Pakistan’s flora is enriched with phytochemicals due to a diverse range of land. Phytochemicals, including volatile and non-volatile compounds, have captured much interest due to their numerous health advantages and significance in daily diet. The present study aimed to conduct in-depth metabolomic profiling of Pakistani-grown fenugreek leaves (Trigonella foenum-graecum), fennel seeds (Foeniculum vulgare), mint leaves (Mentha royleana), coriander seeds (Coriandrum sativum) and basil leaves (Ocimum basilicum) by using liquid chromatography−mass spectrometry (LC-MS/MS) and gas chromatography−mass spectrometry (GC-MS). The first study was conducted to optimize extraction using different solvents (methanol, ethanol, chloroform, acetone, and water). Total phenolic content (TPC), total flavonoid content (TFC), and total condensed tannins (TCT) were quantified along with the antioxidant and anti-diabetic activities. The highest TPC (12... [more]
A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept
Ping Ma, Yuan Liu, Yiyang Chen
February 10, 2024 (v1)
Keywords: data filtering, gradient search, multivariate system, parameter identification, partial coupling
Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method’s effectiveness.
Phosphorus Recovery from Wastewater Aiming Fertilizer Production: Struvite Precipitation Optimization Using a Sequential Plackett−Burman and Doehlert Design
Paulo Victor Campos, Rômulo Simões Angélica, Lênio José Guerreiro de Faria, Simone Patrícia Aranha Da Paz
January 12, 2024 (v1)
Keywords: combined responses, DOE, phase identification, thermochemical analysis
The precipitation of struvite from wastewater is a potential alternative for the recovery of nutrients, especially phosphorus, which is an essential macronutrient for agriculture but can be harmful to the environment when improperly disposed of in water bodies. In addition, struvite has elements of great added value for agricultural activity (P, N, and Mg) and is, therefore, considered a sustainable alternative fertilizer. In its formation process, several intervening physicochemical factors may be responsible for the production yield levels. Optimization processes can help to define and direct the factors that truly matter for precipitation. In this context, a sequential design of experiments (DOE) methodology was applied to select and optimize the main struvite precipitation factors in wastewater. Initially, a screening was performed with eight factors with the aid of Plackett−Burman design, and the factors with a real influence on the process were identified. Then, a Doehlert design... [more]
Identification of an Antimicrobial Protease from Acanthamoeba via a Novel Zymogram
Alvaro de Obeso Fernández del Valle, Luis Javier Melgoza-Ramírez, María Fernanda Esqueda Hernández, Alfonso David Rios-Pérez, Sutherland K. Maciver
January 12, 2024 (v1)
Keywords: Acanthamoeba, antimicrobial, encystment, protease, zymogram
Proteases play a role in different processes for protozoans and for the free-living amoeba Acanthamoeba. Some of these processes are related to pathogenicity and to encystment. In this study we describe the discovery of a protease with antimicrobial activity produced by Acanthamoeba. To identify it, we developed a novel zymogram using bacteria as an in-gel substrate that can help identify proteins capable of bacterial degradation. We used chromatography to isolate the proteases and showed that it quickly degrades in the environment. Additionally, we identified overexpressed proteases during encystment. The study of proteases from Acanthamoeba can serve several purposes including new antimicrobial proteins that the amoeba can use for potentially predigesting prokaryotes. Secondly, it can help with the identification of potential new therapies against Acanthamoeba infection.
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