Identification Of Critical Parameters Research Articles

An innovative environmental tool to evaluate the sustainability of anthropogenic processes: the tetrahedron approach.

The Tetrahedron approach is a new environmental tool adapted to assess the sustainability of anthropogenic processes. This tool is based on a four-step methodology that includes (a) the identification of critical parameters, (b) evaluation through the Tetrahedron Parameter Global Evaluator, (c) construction of a tetrahedron diagram based on the final scores and (d) quantitative estimation of the global sustainability. The Tetrahedron incorporates various aspects of sustainability, including economic, social and environmental factors, and provides a comprehensive framework for evaluating the impact of human activities. This article presents the methodology and application of the Tetrahedron in determining the sustainability of five case studies: CO2 capture, unconventional methanol production, the Solvay process, CO2-alcoholic fermentation process strategy and the CO2-Rumen fermentation process strategy. The results demonstrate the Tetrahedron as an effective and reliable tool to quantify the sustainability of anthropogenic processes and to promote sustainable practices across various industries and sectors. The Tetrahedron offers several advantages over other environmental assessment tools, including holistic approach, simplicity and flexibility.

Optimizing Space Telescopes’ Thermal Performance through Uncertainty Analysis: Identification of Critical Parameters and Shaping Test Strategy Development

The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope developed by the Instituto de Astrofísica de Canarias (IAC), this paper reports an uncertainty analysis on a thermal model aimed at improving cost savings in the future testing phases. The primary objective was to identify critical parameters impacting thermal performance and reduce overdesign. Employing the Statistical Error Analysis (SEA) method across several operational scenarios, the research identifies key factors, including the Earth’s infrared temperature and albedo, and the spacecraft’s attitude and environmental conditions, as the variables with major influences on the system’s thermal performance. Ultimately, the findings suggest that uncertainty-based analysis is a potent tool for guiding thermal control system design in space platforms, promoting efficiency and reliability. This methodology not only provides a framework for optimizing thermal design and testing in space missions but also ensures that instruments like the VINIS telescope maintain optimal operating temperatures in diverse space environments, thereby increasing mission robustness and enabling precise resource allocation.

MHD Natural Convection and Sensitivity Analysis of Ethylene Glycol Cu-Al2O3 Hybrid Nanofluids in a Chamber with Multiple Heaters: A Numerical Study of Lattice Boltzmann Method

The present work investigates the magnetohydrodynamic (MHD) convective heat transport of hybrid nanofluids in a chamber with multiple heaters (such as hot microchips). The multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) and graphics processing unit (GPU) computing are used here. The present study is important due to its relevance to real-world applications, the use of advanced simulation techniques, the consideration of hybrid nanofluids, the inclusion of magnetohydrodynamics, and the identification of critical parameters influencing heat transfer. Thermally homogeneous blocks are set at the bottom of a rectangular enclosure filled with ethylene glycol Cu-Al2O3 hybrid nanofluids with temperature-dependent viscosity. The cold temperature of the enclosure’s left and right walls and the bottom and top surfaces is kept at adiabatic conditions. The numerical outcomes for the various parameters Rayleigh number (104≤Ra≤106), volume fraction (0.00≤ϕ≤0.04), Hartmann number (0≤Ha≤60), and viscosity variation parameter (0≤ε∗≤5) are presented in terms of streamlines, isotherms, and the peripheral local and average Nusselt numbers for the heated chips. The results demonstrated that inside the chamber, the Rayleigh number (Ra), the Hartmann number (Ha), and the volume fraction of nanoparticles (ϕ) have the highest impact on the heat transfer rate for hybrid nanofluid. For increased Ha from 0 to 20, while ϕ=0.0 and Ra=106, average Nusselt number decreased with 13.65%. For the same case, if the volume fraction was increased to ϕ=0.04, then the average Nusselt number decreased 14%. Finally, a sensitivity analysis was done to analyze the system’s correctness and effectiveness to determine the significance of the specified parameters.

Nanomaterial-Enhanced Sizings: Design and Optimisation of a Pilot-Scale Fibre Sizing Line

This study focuses on the development of a pilot-scale sizing line, including its initial design and installation, operational phases, and optimization of key process parameters. The primary objective is the identification of critical parameters for achieving a uniform sizing onto the fibres and the determination of optimal conditions for maximum production efficiency. This investigation focused on adjusting the furnace desizing temperature for the removal of commercial sizing, adjusting the drying temperature, as well as optimizing the corresponding residence time of carbon fibres passing through the furnaces. The highest production rate, reaching 1 m sized carbon fibres per minute, was achieved by employing a desizing temperature of 550 °C, a drying temperature of 250 °C, and a residence time of 1 min. Furthermore, a range of sizing solutions was investigated and formulated, exploring carbon-based nanomaterial types with different surface functionalizations and concentrations, to evaluate their impact on the surface morphology and mechanical properties of carbon fibres. In-depth analyses, including scanning electron microscopy and contact angle goniometry, revealed the achievement of a uniform coating on the carbon fibre surface, leading to an enhanced affinity between fibres and the polymeric epoxy matrix. The incorporation of nanomaterials, specifically N2-plasma-functionalized carbon nanotubes and few-layer graphene, demonstrated notable improvements in the interfacial shear properties (90% increase), verified by mechanical and push-out tests.

Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.

Multi-level framework for optimal operation of gasifier system utilising crop residues from small and medium-scale farms

Lack of techno-economic framework for optimal gasification and the identification of critical parameters for optimal operations is one of the major challenges restricting the gasification of crop residues. This study aims to develop an optimal techno-economic framework for the gasification of crop residues from clustered small/medium-scale farms. The developed model was applied to a case study in Adiembra, a farming community for a 10-kW gasifier engine system. Eight scenarios of individual feedstock and their blends were considered. The results revealed specific fuel consumption ranging from 1.79 – 3.53 kg/kWh. The economic analysis showed marginal profitability except for rice husk and straw which are not profitable. At the current grid electricity price, the minimum level of subsidies required to ensure the financial viability of the feedstocks is within the range of 30 – 70 % of the investment cost based on the various feedstock scenarios considered. The study revealed individual feedstocks with the best technical and economic prospects for optimal gasification to be cocoa pod husk, maize stalk and husk, maize cobs, rice straw and rice husk in the order of best to worst. The use of feedstock blends generally improved the overall syngas characteristics and financial viability. A total number of farms ranging between 107 – 532 are required to be clustered within a radius of 0.74 – 2.12 km with a cluster radius greater than 3.91 km not being financially viable. The fraction of each feedstock type in the blends were optimised with corresponding increase in syngas generation within the range of 9 – 35 % and decrease in the required number of farms within the range of 30 – 57 %. The outcome of the study demonstrates that sustainable gasification of crop residues for minigrid electricity generation requires co-gasification of various residue types, valorisation of by-products and increase in the current feed-in-tariff rate in Ghana.

Deep borehole disposal as a potential solution for high-level waste (HLW) and spent nuclear fuel (SNF) in Norway – current status and a first generic safety assessment

Abstract. Deep borehole disposal (DBD) is currently being investigated as a potential solution for the disposal of the existing spent nuclear fuel (SNF) in Norway. Over the past years, BGE TECHNOLOGY GmbH (BGE TEC), together with the Finnish companies A-Insinöörit Oy (AINS Group), Mitta Oy and VTT Technical Research, has supported the Norwegian agency Norwegian Nuclear Decommissioning (NND) in the development of a deep borehole concept, including work on different technical details. At the end of 2022, the consortium was able to win a follow-up contract to further deepen and advance the work. For this second framework agreement, additional companies were added to further strengthen the expertise. The Geological Survey of Finland (GTK), Posiva Solutions Oy (PSOY) and Rambøll are now part of the new consortium group, which works under the acronym GeoReN (Geological Repositories for Norway). This paper provides an overview of the work carried out in the past for the deep borehole concept in Norway. In detail, this includes a basic description of the concept developed for the waste that originates from different institutions. Initial calculations indicate that a 69 BSK-R canister may be sufficient for the waste inventory to be considered. As the current conceptual borehole design provides the basis for most of the other work carried out, it will be presented and discussed within the paper. Furthermore, as part of the concept development, a first generic post-closure safety assessment was also performed for the DBD concept. The main results of the long-term calculations are discussed and presented in this paper as well. The main objectives of the generic safety assessment were the identification of critical parameters and assumptions for the model of the DBD repository and the performance of a first assessment of possible consequences of the potential post-closure release of radionuclides from the borehole. To be able to give a prediction at the present stage of the project, the assessment used conservative assumptions, models and data. The preliminary results for the normal evolution of the deep borehole repository indicate that highly mobile radionuclides, such as I-129, Cl-36, Ni-59 and Se-79, are the major dose contributors and that the expected potential radiological impact on future generations associated with these releases is extremely small. In addition, alternative scenarios have been assessed, which assume a hydraulic gradient leading to advective flow either upward through the borehole and surrounding excavation damaged zone (EDZ) or between certain parts of the borehole and the surface via a transmissive fracture system. Despite the pessimistic assumptions chosen for these scenarios, the maximum dose rates are significantly lower than the expected regulatory limit. However, it needs to be pointed that the present generic safety assessment is still on a conceptual level. With more detailed planning and updated information regarding the inventory, the geological environment, and the integrity and performance of the engineered barriers, repeated safety assessments will need to be performed to derive more substantiated results.

Robust probabilistic modelling of mould growth in building envelopes using random forests machine learning algorithm

Probabilistic methods can be used to account for uncertainties in hygrothermal analysis of building envelopes. This paper presents methods for robust mould reliability analysis and identification of critical parameters. Mould indices are calculated by probabilistic hygrothermal analysis, followed by the application of the "Finnish mould growth model." To increase the robustness of the mould growth analysis, a random forests metamodel is first trained on the dataset and then used to expand the number of simulations. Finally, the reliability is calculated based on the probability of exceeding a given maximum mould index limit state. Critical parameters are identified through a sensitivity analysis based on linear and non-linear dependencies between inputs and maximum mould index. The methods are demonstrated by analysing three external wall assemblies. In conclusion, the mould reliability analysis method helps to assess the robustness of the hygrothermal analysis and mould assessment by investigating the influence of hygrothermal variables' uncertainties on the maximum mould index. By combining a metamodel with probabilistic analysis, it is possible to significantly reduce the amount of time required to evaluate a large number of scenarios.

Quality by design approach for enantiomeric evaluation by RP-HPLC method of Rivaroxaban and its chiral impurity

Rivaroxaban is an anticoagulant that presents as a mechanism of action the direct and selective inhibition of activated coagulation factor X (FXa). This molecule has a chiral center but only the enantiomer (S)-Rivaroxaban presents pharmacological activity being (R)-Rivaroxaban an impurity. Quality by Design (QbD) is a fundamental model of pharmaceutical quality used in the development of products and processes. The QbD approach allows the identification of critical parameters to predefined quality attributes and is conducted by risk analysis and design of experiments (DoE) promoting a broader knowledge about the analytical method which is relevant for methods of quantification of impurities. The chiral analysis presents as a challenge the separation of compounds with identical physical–chemical properties. Thus, it is fundamental the use of specific techniques to resolve enantiomers, demonstrating the need for the development of appropriate and reliable methods for this purpose. This work aimed to develop and validate an analytical method capable of separate and quantify S-Rivaroxaban (S-RIV) and its enantiomeric impurity R-Rivaroxaban (R-RIV). For that goal, it was used the QbD approach for the development of a suitable and reproductive method using high-performance liquid chromatography (HPLC). The chromatographic conditions were a Chiralpak® AD-RH (150 × 4.6 mm; 5 µm) column, mobile phase composed of acetonitrile (ACN): water pH 4.5 (92:8 v/v) with a flow of 0.35 mL/min, detection at 250 nm, a temperature of 40 °C and total analysis time of 12 min. The analytical method was validated following the official guidelines, demonstrating selectivity, linearity, precision, accuracy and sensitivity presenting a limit of quantification (LOQ) of 0.68 µg/mL for S-RIV and 1.0 µg/mL for the chiral impurity. After the development and validation steps, the presence of R-RIV impurity in marketed tablets was performed using the proposed method. This study resulted on the development and validation of a reliable and appropriate chiral analytical method for its aim bringing a valuable contribution to quality control routines.

Enabling predictive maintenance in high-speed manufacturing machines through critical parameter identification with sensor data

Enabling predictive maintenance in high-speed manufacturing machines through critical parameter identification with sensor data

Identification of critical parameters influencing resistance performance of amphibious vehicles based on a SM-SA method

The design of high-speed amphibious vehicles needs to consider more factors compared to ships. The efficiency of design and optimization of parameters will not be realized in the absence of a feasible parametric model and design criterion. This paper provided a recognition method for the critical factors that significantly affect amphibious vehicles' resistance. Firstly, an initial parametric model of amphibious vehicles was established, and the resistance coefficients were acquired through numerical simulations. The principal component variables of initial data were extracted by principal component analysis (PCA). Then the functional relations between resistance and principal component variables were obtained respectively through artificial neural network (ANN) and nonlinear polynomial fitting (NPF). Next, two surrogate models were employed to analyze the sensitivity of the resistance to initial parameters. The identified sensitive parameters include the trim angle, loss of waterplane area, and some principal dimensions coefficients. The variation of parameters’ sensitivity and their interactions were recognized when parameters are located in different regions. Ultimately, the resistance surrogate model was constructed with critical parameters, enabling the rapid optimization of parameter scheme. Compared with the initial scheme, the optimized scheme achieved significantly reduction of resistance. The extraction and optimization method for critical parameters in this paper provides reference for the design of amphibious vehicles.

Impact of PWM Voltage Waveforms in High-Speed Drives: A Survey on High-Frequency Motor Models and Partial Discharge Phenomenon

The insulation system’s dielectric of the electric motor is very often subjected to severe electrical stress generated by the high dv/dt seen at the machine’s terminals. The electrical stress and high reflected wave transient overvoltage are even more evident in case of high-speed machines fed by high-frequency (HF) converters featuring very fast wide-bandgap devices. They are promoting the occurrence of partial discharges and consequently accelerate ageing. As this is serious issue and the main cause of the drive failure, it is important to analyse and characterise the surges at the motor terminals. Several HF models of motors have been proposed in the literature for this purpose. This article presents a survey on HF motor models, which is crucial in understanding and studying the most critical parameter identification and overvoltage mitigation techniques. Moreover, it offers a comparison of the models’ main features as well as a comparison with the experimental voltage waveform at motor terminals. A general overview of the partial discharge (PD) phenomenon is also provided, as it is favoured by HF operation and together with HF motor modelling provides key insights to the insulation ageing issue. In particular, an analysis of the effects of PWM waveform affecting insulation is given, as well as useful methods for developing strategies for the inspection and maintenance of winding insulation.

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

Systematic uncertainty analysis can be used to quantitatively evaluate variation in model outputs and identify the critical sources of uncertainty to improve the reliability and stability of a system. To analyze the effects of uncertainties in transit networks that may be caused by probabilistic travel demand, congestion, or vehicle frequencies, this study develops a sensitivity-based uncertainty analysis approach as a post-analysis tool for equilibrium transit systems. The congestion effect is considered in the waiting time and in-vehicle travel time of a passengers’ route-choice model. The hyperpath concept is used to manage passengers’ riding strategies due to the common-line problem at transit stops. A hyperpath-based gradient projection (GP) solution algorithm is developed for the solution of the variational inequality formulation of the transit equilibrium assignment problem (TEAP). A restricted sensitivity analysis approach originally developed for road networks is re-developed for the TEAP in transit networks. An analytical sensitivity-based approach is derived to conduct uncertainty analysis for the TEAP, which enables the simultaneous propagation of uncertainties from different input sources to the model outputs. Numerical examples are provided for the following purposes. (1) To demonstrate three applications of the sensitivity analysis of the TEAP, namely the perturbed solution estimation problem, the critical parameter identification problem, and the paradox analysis problem. (2) To illustrate the use of uncertainty analysis of the TEAP, such as estimating the variance and confidence level of model outputs with respect to various model inputs/parameters as random variables, and ranking the importance of arcs using sensitivity-based uncertainty analysis. (3) To demonstrate the applicability of the proposed approach to real transit networks. The findings demonstrate not only the importance of the analytical sensitivity analysis development for the TEAP, but also for the practical applications of sensitivity and uncertainty analyses.

Quantitative Evaluation of Dendritic Nanoparticles in Mice: Biodistribution Dynamics and Downstream Tumor Efficacy Outcomes.

A physiologically based pharmacokinetic model was developed to describe the tissue distribution kinetics of a dendritic nanoparticle and its conjugated active pharmaceutical ingredient (API) in plasma, liver, spleen, and tumors. Tumor growth data from MV-4-11 tumor-bearing mice were incorporated to investigate the exposure/efficacy relationship. The nanoparticle demonstrated improved antitumor activity compared to the conventional API formulation, owing to the extended released API concentrations at the site of action. Model simulations further enabled the identification of critical parameters that influence API exposure in tumors and downstream efficacy outcomes upon nanoparticle administration. The model was utilized to explore a range of dosing schedules and their effect on tumor growth kinetics, demonstrating the improved antitumor activity of nanoparticles with less frequent dosing compared to the same dose of naked APIs in conventional formulations.

MEM vs. FEM: practical crashworthiness insights for macro element modelling applied to sub-assembly and full vehicle automotive structures

This paper proposes a modelling approach for integral vehicle structures, applied to frontal crash loading, based on the Macro element approach. Addressing the idealisation of complex sub-structures and full vehicle was through identification of critical parameters for conversion of validated FEM into MEM equivalents through sensitivity analyses. Two examples of impact onto rigid barriers are presented; 1). Frontal crash energy management system (consisting crush-can and longitudinal engine rail), impacting at 8.6 m/s and 2). A complete vehicle impact at 56 km/hr (15m/s). Both case studies predict key features of collapse, with force-time histories agreeing within ±10–15% against FEM. Case study 1 required a 3 second solution time versus 1.5 h (8CPUS) mass-scaled FEM (105k element). For Case study 2, MEM required 7.5mins versus 16.5 hrs for a 3 M element FEM vehicle. For all simulations, LS-DYNA R10.0 and Visual Crash Studio R4.0 used. Developing a framework to overcome accuracy/stability problems, together with issues related to robustness and error reduction is discussed. Model complexity was progressive, involving a-priori knowledge of collapse and/or analysing several sub-assemblies to guide idealisation. The level of agreement demonstrates the advantages of MEM as a complementary method to support conceptual vehicle design and offers significant advantages for design exploration, particularly across multiple crash certification cases. Highlights Idealisation of complex sub-structures into Macro element modelling (MEM) equivalents using high fidelity finite element models (FEM) Methodology based on critical parameters for conversion of validated FEM into MEM Proposed MEM conversion for complete vehicle frontal impact onto a rigid barrier Correlation of critical parameters with structural behaviour through sensitivity analyses MEM error in force-time histories within 10–15%, relative to high fidelity FEM FEM Solution times reduced from several hours to several minutes for MEM equivalents

Free vibration behaviour of thin-walled concrete box-girder bridge using Perspex sheet experimental model

Purpose: Curved box-girder bridges offers an excellent solution to the problems associated with traffic congestion. However, owing to their complex geometry, they are subjected to shear lag, torsional warping and cross-sectional distortion, which must be assessed in their study and design. Furthermore, the dynamic behaviour of curved bridges adds to the complexity of the issue, emphasizing the importance of studying free vibration. The purpose of this study is to numerically model the concrete curved box-girder bridge considering torsional warping, distortion and distortional warping effects and to identify key parameters that influence the free vibration response of the box-girder bridge by validating it with experimental and analytical studies. Design/methodology/approach: The concrete bridge is numerically modelled by means of computationally effective thin-walled box-beam finite elements that consider torsional warping, distortion and distortional warping, which are prominent features of thinwalled box-girders. The free vibration analysis of the concrete curved box-girder bridge is performed by developing a finite element based MATLAB program. Findings: The identification of critical parameters that influence the free vibration behaviour of curved thin-walled concrete box-girder bridges is one of the main findings of the study. Each parameter and its effect has been extensively discussed. Research limitations/implications: The study limits for the preliminary design phase of thin-walled box-girder bridge decks, where a complete three-dimensional finite element analysis is unnecessary. The current approach can be extended to future research using a different method, such as finite element grilling technique on multi-span curved bridges having unequal span.

A state-of-the-art review of vertical ground motion (VGM) characteristics, effects and provisions

Previous earthquake events have shown that the vertical component of strong ground motion has been proved to damage or totally destroy the structures. But in seismic resistant design of structures, effects of horizontal ground motions are mainly considered and effects of vertical ground motions (VGMs) are ignored. However, the field evidence of damaged structures indicates that the VGM was more significant particularly for near-fault earthquakes. Therefore, the addition of vertical component in seismic design has been recognized in the literature. In seismic resistant design of structures, horizontal ground motions are extensively studied and used for designing of structures. However, VGMs are not considered of more importance as horizontal one. But with the past near-fault earthquakes and their damage evidence effects have increased the awareness of VGMs. In this paper, the critical reviews of the literature associated with VGM are explained and reviewed. The important characteristics like vertical response spectra, near-fault influence, frequency influence and time lag between peak vertical and peak horizontal ground motion, vertical response period influence and local site conditions are explained. The provisions in latest national and international standards and the field evidence of damaging effects caused by VGM are also explained. Considering all the above parameters associated with VGM, the past studies on different types of structures are reviewed. Finally, a methodology for identification of critical parameters and future scope on VGM is proposed. This study leads to some important conclusions that VGM needs to be included in analysis and design.

Techno-economic and environmental analysis of biomass-based hybrid energy systems: A case study of a Western Himalayan state in India

India has a profuse reserve of renewables to contribute to energy sectors. In this study, a techno-economic and environmental analysis are carried out for an off-grid hybrid system as a case study in the western Himalayan region. Seven different hybrid systems with and without battery storage are simulated. The PV/Biomass gasifier/Battery based hybrid energy system is found to be the optimal configuration with the least cost of energy $0.185/kWh at an estimated total net present cost $76080 with 59% biomass and 41% PV contribution. A saving of 27.8 Mt CO2/year (equated to the diesel-only system) is predicted. Finally, sensitivity analysis is performed for critical parameter identification. Five (Solar radiation, biomass price, annual interest rate, biomass gasifier lifetime, and annual capacity shortage) out of nine sensitivity parameters is found to be critical. The outcome of the study is of importance to designers, researchers, policymakers on the latest design constraints and relevant policies for biomass-based hybrid systems.

Parameter optimization of environmental technologies using a LCA-based analysis scheme: A bioaugmentation case study

Life cycle assessment (LCA) has proven to be a useful tool in assessing environmental technologies in a retrospective manner. To fully uncover the environmental improvement potential while advancing technologies under technical and environmental constraints, this study recommended approaching the LCA proactively to assess the progress of parameter optimization before determining critical parameters. To that end, the present work introduced a multimethod eight-step (MMES) analysis scheme, which included an integration of LCA with Plackett-Burman multifactorial design, central composite design, and multi-objective optimization. By creating a large number of scenarios through experimental design, we jointly optimized technical efficiency and environmental sustainability, which allowed for the identification of critical parameters that likely had contradictory influences on different objectives. Through a case study concerning the bioaugmentation of constructed wetland (CW), we applied the MMES scheme to optimize the culture conditions of the strain Arthrobacter sp. ZXY-2 for enhanced atrazine removal. The results showed that, by reducing the Na2HPO4·12H2O concentration from 6.5 g/L to 6 g/L in the culture condition, we decreased the freshwater ecotoxicity potential and maintained a high level of atrazine removal. Regarding the production process of microbial inocula, the strain ZXY-2 grown at the optimized culture reduced the total environmental impact from 13% to 50% compared with the original culture and helped the CW exhibit more favorable atrazine-removal performance. Taken together, the case study demonstrated the effectiveness of using the MMES scheme for parameter optimization of environmental technologies. For future development, the MMES scheme should extend the application to more fields and refine uncertainty management.

Identification of Critical Parameters for the Design of Energy Management Algorithms for Li-Ion Batteries Operating in PV Power Plants

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step.