Margie and Bill Klesse College of Engineering and Integrated Design Faculty Research

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/829

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A 3D Printed Linear Pneumatic Actuator for Position, Force and Impedance Control
(2018-05-24) Krause, Jeremy; Bhounsule, Pranav
Although 3D printing has the potential to provide greater customization and to reduce the costs of creating actuators for industrial applications, the 3D printing of actuators is still a relatively new concept. We have developed a pneumatic actuator with 3D-printed parts and placed sensors for position and force control. So far, 3D printing has been used to create pneumatic actuators of the bellows type, thus having a limited travel distance, utilizing low pressures for actuation and being capable of only limited force production and response rates. In contrast, our actuator is linear with a large travel distance and operating at a relatively higher pressure, thus providing great forces and response rates, and this the main novelty of the work. We demonstrate solutions to key challenges that arise during the design and fabrication of 3D-printed linear actuators. These include: (1) the strategic use of metallic parts in high stress areas (i.e., the piston rod); (2) post-processing of the inner surface of the cylinder for smooth finish; (3) piston head design and seal placement for strong and leak-proof action; and (4) sensor choice and placement for position and force control. A permanent magnet placed in the piston head is detected using Hall effect sensors placed along the length of the cylinder to measure the position, and pressure sensors placed at the supply ports were used for force measurement. We demonstrate the actuator performing position, force and impedance control. Our work has the potential to open new avenues for creating less expensive, customizable and capable actuators for industrial and other applications.
A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications
(2021-10-13) Kaplan, Halid; Tehrani, Kambiz; Jamshidi, Mo
Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.
A GPU-Accelerated Particle Advection Methodology for 3D Lagrangian Coherent Structures in High-Speed Turbulent Boundary Layers
(2023-06-19) Lagares, Christian; Araya, Guillermo
In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian coherent structures (LCS) in large-scale direct numerical simulation (DNS) datasets across incompressible, supersonic, and hypersonic flow regimes. LCS play a significant role in turbulent boundary layer analysis, and our proposed methodology offers valuable insights into their behavior in various flow conditions. Our novel owning-cell locator method enables efficient constant-time cell search, and the algorithm draws inspiration from classical search algorithms and modern multi-level approaches in numerical linear algebra. The proposed method is implemented for both multi-core CPUs and Nvidia GPUs, demonstrating strong scaling up to 32,768 CPU cores and up to 62 Nvidia V100 GPUs. By decoupling particle advection from other problems, we achieve modularity and extensibility, resulting in consistent parallel efficiency across different architectures. Our methodology was applied to calculate and visualize the FTLE on four turbulent boundary layers at different Reynolds and Mach numbers, revealing that coherent structures grow more isotropic proportional to the Mach number, and their inclination angle varies along the streamwise direction. We also observed increased anisotropy and FTLE organization at lower Reynolds numbers, with structures retaining coherency along both spanwise and streamwise directions. Additionally, we demonstrated the impact of lower temporal frequency sampling by upscaling with an efficient linear upsampler, preserving general trends with only 10% of the required storage. In summary, we present a particle search scheme for particle advection workloads in the context of visualizing LCS via FTLE that exhibits strong scaling performance and efficiency at scale. Our proposed algorithm is applicable across various domains, requiring efficient search algorithms in large, structured domains. While this article focuses on the methodology and its application to LCS, an in-depth study of the physics and compressibility effects in LCS candidates will be explored in a future publication.
A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
(2016-01-19) Li, Zhaoxuan; Rahman, SM Mahbobur; Vega, Rolando; Dong, Bing
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.
A Hybrid TOPSIS-Structure Entropy Weight Group Subcontractor Selection Model for Large Construction Companies
(2023-06-16) Gao, Ce; Elzarka, Hazem; Yan, Hongyan; Chakraborty, Debaditya; Zhou, Chunmei
The selection of suitable subcontractors for large construction companies is crucially important for the overall success of their projects. As the construction industry advances, a growing number of criteria need to be considered in the subcontractor selection process than simply considering the biding prices. This paper proposed a hybrid multi-criteria structure entropy weight (SEW)—TOPSIS group decision-making model that considers 10 criteria. The proposed model was able to handle large amount of subcontractors’ performance data that were collected in different types. Additionally, the model can integrate experts’ judgments while accounting for their varying level of expertise and correcting for their biases. This paper also provided a case study to demonstrate the proposed model’s effectiveness and efficiency, as well as its applicability of large construction companies. While this study was applied to construction subcontractors’ selection, the proposed methodology can also be easily extended to various decision-making scenarios with similar requirements.
A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting
(2017-03-23) Richardson, Walter; Krishnaswami, Hariharan; Vega, Rolando; Cervantes, Michael
With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV. The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.
A Miniature 3D Printed On-Off Linear Pneumatic Actuator and Its Demonstration into a Cartoon Character of a Hopping Lamp
(2019-10-17) Nall, Christian L.; Bhounsule, Pranav A.
Although 3D printing has been extensively used to create passive machines and mechanisms, 3D printing of actuators is a relatively new concept. 3D printing of actuators allows greater customization, accelerates the design and development, and consequently saves time and money. We present the design and fabrication of a 3D printed, miniature size, double-acting, On-Off type, linear pneumatic actuator. The actuator has an overall length of 8 cm, a bore size of 1.5 cm, and a stroke length of 2.0 cm. The overall weight is 12 gm and it generates a peak output power of 2 W when operating at an input air pressure of 40 psi (275.79 kPa). This paper demonstrates novel methods to solve the challenges that arise during fabrication that include: (1) chemical post-processing to achieve airtight sealing and a smooth surface finish, (2) strategic placement of a metallic part within 3D printed plastic for higher strength, (3) design of an airtight seal between the cylinder and piston head, (4) chemical bonding of printed parts using adhesive, and (5) use of a lubricant to reduce friction and improve force generation. The power-to-weight ratio of our actuator is comparable to that of high-end commercial actuators of similar size. The utility of the actuator is demonstrated in a series of jumping experiments with the actuator and by incorporating the actuator into a hopping robot inspired by Disney/Pixar Luxo lamp. We conclude that 3D printed pneumatic actuators combine the high power of pneumatics with the low weight of plastics, and structural strength through the selective placement of metal parts, thus offering a promising actuator for robotic applications.
A Modular Multilevel Converter with Power Mismatch Control for Grid-Connected Photovoltaic Systems
(2017-05-17) Duman, Turgay; Marti, Shilpa; Moonem, M. A.; Abdul Kader, Azas Ahmed Rifath; Krishnaswami, Hariharan
A modular multilevel power converter configuration for grid connected photovoltaic (PV) systems is proposed. The converter configuration replaces the conventional bulky line frequency transformer with several high frequency transformers, potentially reducing the balance of systems cost of PV systems. The front-end converter for each port is a neutral-point diode clamped (NPC) multi-level dc-dc dual-active bridge (ML-DAB) which allows maximum power point tracking (MPPT). The integrated high frequency transformer provides the galvanic isolation between the PV and grid side and also steps up the low dc voltage from PV source. Following the ML-DAB stage, in each port, is a NPC inverter. N number of NPC inverters’ outputs are cascaded to attain the per-phase line-to-neutral voltage to connect directly to the distribution grid (i.e., 13.8 kV). The cascaded NPC (CNPC) inverters have the inherent advantage of using lower rated devices, smaller filters and low total harmonic distortion required for PV grid interconnection. The proposed converter system is modular, scalable, and serviceable with zero downtime with lower foot print and lower overall cost. A novel voltage balance control at each module based on power mismatch among N-ports, have been presented and verified in simulation. Analysis and simulation results are presented for the N-port converter. The converter performance has also been verified on a hardware prototype.
A Modular Multilevel Converter with Power Mismatch Control for Grid-Connected Photovoltaic Systems
(2017-05-17) Duman, Turgay; Marti, Shilpa; Moonem, M. A.; Abdul Kader, Azas Ahmed Rifath; Krishnaswami, Hariharan
A modular multilevel power converter configuration for grid connected photovoltaic (PV) systems is proposed. The converter configuration replaces the conventional bulky line frequency transformer with several high frequency transformers, potentially reducing the balance of systems cost of PV systems. The front-end converter for each port is a neutral-point diode clamped (NPC) multi-level dc-dc dual-active bridge (ML-DAB) which allows maximum power point tracking (MPPT). The integrated high frequency transformer provides the galvanic isolation between the PV and grid side and also steps up the low dc voltage from PV source. Following the ML-DAB stage, in each port, is a NPC inverter. N number of NPC inverters’ outputs are cascaded to attain the per-phase line-to-neutral voltage to connect directly to the distribution grid (i.e., 13.8 kV). The cascaded NPC (CNPC) inverters have the inherent advantage of using lower rated devices, smaller filters and low total harmonic distortion required for PV grid interconnection. The proposed converter system is modular, scalable, and serviceable with zero downtime with lower foot print and lower overall cost. A novel voltage balance control at each module based on power mismatch among N-ports, have been presented and verified in simulation. Analysis and simulation results are presented for the N-port converter. The converter performance has also been verified on a hardware prototype.
A Multimodal Appraisal of Zaha Hadid's Glasgow Riverside Museum—Criticism, Performance Evaluation, and Habitability
(2023-01-09) Salama, Ashraf M.; Salingaros, Nikos A.; MacLean, Laura
High-profile projects promoted by governments, local municipalities, and the media do not always meet program requirements or user expectations. The Riverside Museum in Glasgow by Zaha Hadid Architects, which has generated significant discussion in the media, is used to test this claim. A multimodal inquiry adopts three factors: criticism, performance evaluation, and habitability. Results from this method are then correlated with visual attention scans using software from 3M Corporation to map unconscious user engagement. A wide spectrum of tools is employed, including a walking tour assessment procedure, contemplation of selected settings, navigational mapping, and assessing user emotional experiences. Key aspects of the design and spatial qualities of this museum are compared with an analysis of critical writings on how the project was portrayed in the media. Further, we examine socio-spatial practices, selected behavioral phenomena, and the emotional experiences that ensue from users' interaction with the building and its immediate context. The findings suggest design shortcomings and, more worrisome, that spatial qualities relevant to users' experiences do not seem to have been met. In going beyond the usual method of analysis, we apply new techniques of eye-tracking simulations, which verify results obtained by more traditional means. An in-depth analysis suggests the need for better compatibility between the imagined design ideas and the actual spatial environments in use.
A Neural Network Powered Solution Approach for Computationally Expensive Mixed Integer Programs for Bio Jet-fuel Supply Chain Network Design
(Association for Computing Machinery, 2023-05-09) Keith, Kolton; Castillo-Villar, Krystel K.; Alaeddini, Adel
Bio jet fuels derived from feedstock offer a sustainable alternative to meeting energy needs. Modeling supply chains that produce said fuels can lead to computationally prohibitive mixed integer linear programs (MILP) that consider optimal facility location and materials routing. The present work proposes an iterative machine learning-based hybrid solution procedure that transfers some of the responsibility of facility location to the learner. First, given a random selection of facility locations, a collection of model solutions is generated. Next, a neural network is fit to the collection of solutions, with facility locations being the input and total supply chain (SC) costs as the output. Then, the next set of locations is selected to optimize the predicted output of the neural network. Finally, the MILP optimization model is called to test the selected locations, and the results are fed back into the neural network and the process is repeated. Numerical experimentation demonstrates the proposed solution procedures yield near-optimal solutions with 0.10-0.18% increase in objective function value alongside a 40-65% reduction in computational time.
A New Compton Camera Imaging Model to Mitigate the Finite Spatial Resolution of Detectors and New Camera Designs for Implementation
(2015-10-27) Smith, Bruce
An intrinsic limitation of the accuracy that can be achieved with Compton cameras results from the inevitable fact that the detectors, which comprise the camera, cannot have infinitely-accurate spatial resolution. To mitigate this loss of accuracy, a new imaging model is proposed. The implementation of the new imaging model, however, requires new camera designs. The results of a computer simulation indicate that the new imaging model can produce reasonable images, at least when noiseless simulated data are used. In the future, more work is needed to determine if the use of the new imaging model will improve the imaging capabilities of Compton cameras despite the loss of sensitivity caused by the use of the new camera designs. Regardless of the outcome of this work, the results presented here illustrate that new models for imaging from Compton scatters are possible and motivate the development of further models that could be more advantageous than the ones already developed.
A Novel Mechanically Overdamped Actuator with Adjustable Stiffness (MOD-AwAS) for Safe Interaction and Accurate Positioning
(2017-06-28) Lee, Jae Hoon; Wahrmund, Christian; Jafari, Amir
This paper presents the design and development of a novel mechanically overdamped actuator with adjustable stiffness (MOD-AwAS). The novelty of MOD-AwAS compared to other variable stiffness actuators relates to its mechanical design, which prevents oscillations at the output link. Almost all variable stiffness actuators have an overshooting problem that require a sophisticated control algorithm to be able to perform accurate positioning. MOD-AwAS can regulate the stiffness from zero to its maximum (theoretically infinite) in less than 0.2 s by changing the position of the pivot point of its lever mechanisms. MOD-AwAS employs only one rotational spring with no pre-deflection, which gives it full accessibility to its energy storage capacity. Experimental results are presented to show the ability of MOD-AwAS to control its position accurately with a wide range of stiffness adjustment.
A Principal Component Analysis in Switchgrass Chemical Composition
(2016-11-04) Aboytes-Ojeda, Mario; Castillo-Villar, Krystel K.; Yu, Tun-hsiang E.; Boyer, Christopher N.; English, Burton C.; Larson, James A.; Kline, Lindsey M.; Labbé, Nicole
In recent years, bioenergy has become a promising renewable energy source that can potentially reduce the greenhouse emissions and generate economic growth in rural areas. Gaining understanding and controlling biomass chemical composition contributes to an efficient biofuel generation. This paper presents a principal component analysis (PCA) that shows the influence and relevance of selected controllable factors over the chemical composition of switchgrass and, therefore, in the generation of biofuels. The study introduces the following factors: (1) storage days; (2) particle size; (3) wrap type; and (4) weight of the bale. Results show that all the aforementioned factors have an influence in the chemical composition. The number of days that bales have been stored was the most significant factor regarding changes in chemical components due to its effect over principal components 1 and 2 (PC1 and PC2, approximately 80% of the total variance). The storage days are followed by the particle size, the weight of the bale and the type of wrap utilized to enclose the bale. An increment in the number of days (from 75–150 days to 225 days) in storage decreases the percentage of carbohydrates by −1.03% while content of ash increases by 6.56%.
A Review of Biomaterials and Associated Performance Metrics Analysis in Pre-Clinical Finite Element Model and in Implementation Stages for Total Hip Implant System
(2022-10-13) Soliman, Md Mohiuddin; Chowdhury, Muhammad E. H.; Islam, Mohammad Tariqul; Musharavati, Farayi; Nabil, Mohammad; Hafizh, Muhammad; Khandakar, Amith; Mahmud, Sakib; Nezhad, Erfan Zal; Shuzan, Md Nazmul Islam; Abir, Farhan Fuad
Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10–15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field.
A Review of Methodological Approaches for the Design and Optimization of Wind Farms
(2014-10-29) Herbert-Acero, José F.; Probst, Oliver; Réthoré, Pierre-Elouan; Larsen, Gunner Chr.; Castillo-Villar, Krystel K.
This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research.
A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides
(2022-04-25) Zou, Yong; Zheng, Chao
The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.
Adsorptive properties and on-demand magnetic response of lignin@Fe3O4 nanoparticles at castor oil–water interfaces
(Royal Society of Chemistry, 2023-01-19) Hasan, Mohammad Jahid; Westphal, Emily; Chen, Peng; Saini, Abhishek; Chu, I-Wei; Watzman, Sarah J.; Ureña-Benavides, Esteban; Vasquez, Erick S.
Lignin@Fe3O4 nanoparticles adsorb at oil–water interfaces, form Pickering emulsions, induce on-demand magnetic responses to break emulsions, and can sequester oil from water. Lignin@Fe3O4 nanoparticles were prepared using a pH-induced precipitation method and were fully characterized. These were used to prepare Pickering emulsions with castor oil/Sudan red G dye and water at various oil/water volume ratios and nanoparticle concentrations. The stability and demulsification of the emulsions under different magnetic fields generated with permanent magnets (0–540 mT) were investigated using microscopy images and by visual inspection over time. The results showed that the Pickering emulsions were more stable at the castor oil/water ratio of 50/50 and above. Increasing the concentration of lignin@Fe3O4 improved the emulsion stability and demulsification rates with 540 mT applied magnetic field strength. The adsorption of lignin@Fe3O4 nanoparticles at the oil/water interface using 1-pentanol evaporation through Marangoni effects was demonstrated, and magnetic manipulation of a lignin@Fe3O4 stabilized castor oil spill in water was shown. Nanoparticle concentration and applied magnetic field strengths were analyzed for the recovery of spilled oil from water; it was observed that increasing the magnetic strength increased oil spill motion for a lignin@Fe3O4 concentration of up to 0.8 mg mL−1 at 540 mT. Overall, this study demonstrates the potential of lignin-magnetite nanocomposites for rapid on-demand magnetic responses to externally induced stimuli.
Advances in Membrane Distillation for Water Desalination and Purification Applications
(2013-01-25) Camacho, Lucy Mar; Dumée, Ludovic; Zhang, Jianhua; Li, Jun-de; Duke, Mikel; Gomez, Juan; Gray, Stephen
Membrane distillation is a process that utilizes differences in vapor pressure to permeate water through a macro-porous membrane and reject other non-volatile constituents present in the influent water. This review considers the fundamental heat and mass transfer processes in membrane distillation, recent advances in membrane technology, module configurations, and the applications and economics of membrane distillation, and identifies areas that may lead to technological improvements in membrane distillation as well as the application characteristics required for commercial deployment.
Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review
(2022-04-22) Silva, Gabriel Marinho e; Campos, Daiane Ferreira; Brasil, José Artur Teixeira; Tremblay, Marcel; Mendiondo, Eduardo Mario; Ghiglieno, Filippo
Monitoring water quality is an essential tool for the control of pollutants and pathogens that can cause damage to the environment and human health. However, water quality analysis is usually performed in laboratory environments, often with the use of high-cost equipment and qualified professionals. With the progress of nanotechnology and the advance in engineering materials, several studies have shown, in recent years, the development of technologies aimed at monitoring water quality, with the ability to reduce the costs of analysis and accelerate the achievement of results for management and decision-making. In this work, a review was carried out on several low-cost developed technologies and applied in situ for water quality monitoring. Thus, new alternative technologies for the main physical (color, temperature, and turbidity), chemical (chlorine, fluorine, phosphorus, metals, nitrogen, dissolved oxygen, pH, and oxidation–reduction potential), and biological (total coliforms, Escherichia coli, algae, and cyanobacteria) water quality parameters were described. It was observed that there has been an increase in the number of publications related to the topic in recent years, mainly since 2012, with 641 studies being published in 2021. The main new technologies developed are based on optical or electrochemical sensors, however, due to the recent development of these technologies, more robust analyses and evaluations in real conditions are essential to guarantee the precision and repeatability of the methods, especially when it is desirable to compare the values with government regulatory standards.

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