Tool For Optimization Of Parameters Research Articles

Evaluating MONICA's capability to simulate water, carbon and nitrogen fluxes in a wet grassland at contrasting water tables

Wet grasslands, which are vital for water and nutrient regulation, are characterised by distinct water, carbon (C) and nitrogen (N) dynamics, and their interactions. Due to their shallow groundwater table, wet grasslands promote a strong interconnection between diverse vegetation and soil water. Researchers have investigated how wet grasslands respond to environmental changes, using various simulation models to understand how these sites contribute to water, C and N dynamics. However, a comprehensive, simultaneous study of all three of these dynamics is still lacking. This study makes use of a grassland lysimeter study with differently managed groundwater levels and employs the process-based MOdel for NItrogen and Carbon dynamics in Agroecosystems (MONICA) to simulate these dynamics. By using SPOTPY (Statistical Parameter Optimization Tool) to optimise the relevant parameters, we find that MONICA performs well in simulating vegetation growth (aboveground biomass), and elements of the water (evapotranspiration), C (gross primary productivity, ecosystem respiration) and N (N in aboveground biomass, nitrate in soil solution, Nitrous oxide emissions) balance, with Willmott's Refined Index of Agreement always larger than 0.35. This level of accuracy demonstrates that MONICA is ready to be applied for scenario simulations of groundwater management and climate change to evaluate their impact on greenhouse gas emissions and long-term carbon storage, as well as water and nitrogen losses in wet grasslands.

Correlating analysis and optimization between hydropower system parameters and multi-frequency oscillation characteristics

This paper aims to study the relationship between multi-frequency oscillation characteristics and parameters of hydro-turbine governing systems (HTGS), as well as improve the system oscillation quantification accuracy and primary frequency regulation performance. Firstly, an HTGS model with multi-frequency oscillation characteristics is established using an actual hydropower system as the object. Then, system oscillation components and the sources of each oscillating link are identified using eigenvalue and correlation analysis. To address the issue that sub-wave interference with dominant oscillation quantization, we innovatively incorporate correlation and frequency indexes into quantification process, and the dominant oscillations of complex HTGS are accurately quantified. On this basis, the detailed partitioning of control parameters is established to obtain the guide graph quickly reflecting the relationship between control parameters and system oscillations. Finally, an integrated optimization strategy with ultra-low frequency oscillation (ULFO) safety and regulation performance constraints is constructed, and control parameters are further optimized under two typical parameter setting methods. The results indicate over 90 % of the stable parameters enable the system to enter ULFO mode, and optimized control parameters utilizing proposed strategy increase regulation speed by 46 % while effectively suppressing ULFO. This paper provides a powerful tool and novel ideas for complex HTGS oscillation analysis and parameter optimization.

FFParam-v2.0: A Comprehensive Tool for CHARMM Additive and Drude Polarizable Force-Field Parameter Optimization and Validation.

Developing production quality CHARMM force-field (FF) parameters is a very detailed process involving a variety of calculations, many of which are specific for the molecule of interest. The first version of FFParam was developed as a standalone Python package designed for the optimization of electrostatic and bonded parameters of the CHARMM additive and polarizable Drude FFs by using quantum mechanical (QM) target data. The new version of FFParam has multiple new capabilities for FF parameter optimization and validation, with an emphasis on the ability to use condensed-phase target data in optimization. FFParam-v2 allows optimization of Lennard-Jones (LJ) parameters using potential energy scans of interactions between selected atoms in a molecule and noble gases, viz., He and Ne, and through condensed-phase calculations, from which experimental observables such as heats of vaporization and free energies of solvation may be obtained. This functionality serves as a gold standard for both optimizing parameters and validating the performance of the final parameters. A new bonded parameter optimization algorithm has been introduced to account for simultaneously optimizing multiple molecules sharing parameters. FFParam-v2 also supports the comparison of normal modes and the potential energy distribution of internal coordinates towards each normal mode obtained from QM and molecular mechanics calculations. Such comparison capability is vital to validate the balance among various bonded parameters that contribute to the complex normal modes of molecules. User interaction has been extended beyond the original graphical user interface to include command-line interface capabilities that allow for integration of FFParam in workflows, thereby facilitating the automation of parameter optimization. With these new functionalities, FFParam is a more comprehensive parameter optimization tool for both beginners and advanced users.

Impact of Building Automation on Indoor Air Quality and HVAC Performance

In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.

Rapid parameter identification of three diode photovoltaic systems using the Cheetah optimizer

This study focuses on accurate parameter identification for solar cells and photovoltaic module simulation using experimental data. To tackle the challenge of modelling these highly nonlinear systems, we propose the novel use of the Cheetah Optimizer (CO) algorithm, inspired by cheetah hunting strategies. The CO algorithm employs mathematical models and randomization parameters to balance exploration and exploitation, avoiding local optima by considering energy limitations. We demonstrate the CO algorithm's effectiveness by applying it to the three-diode model in solar photovoltaic systems, specifically the STP6-120/36 and Photowatt-PWP201 PV modules. Impressively, the CO algorithm achieves remarkably low root mean square error values of 0.0145 A and 0.0019 A, outperforming state-of-the-art methods and ensuring high accuracy. Additionally, it delivers the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, highlighting its exceptional performance. The CO algorithm proves to be a promising tool for precise parameter extraction and optimization, leading to improved modelling and performance of solar photovoltaic systems.

High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point.

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications. Although various domain-specific accelerators have been proposed to address this challenge, most of them only focus on accelerating 2-D CNNs, without considering their computational efficiency on 3-D CNNs. In this article, we propose a unified hardware architecture to accelerate both 2-D and 3-D CNNs with high hardware efficiency. Our experiments demonstrate that the proposed accelerator can achieve up to 92.4% and 85.2% multiply-accumulate efficiency on 2-D and 3-D CNNs, respectively. To improve the hardware performance, we propose a hardware-friendly quantization approach called static block floating point (BFP), which eliminates the frequent representation conversions required in traditional dynamic BFP arithmetic. Comparing with the integer linear quantization using zero-point, the static BFP quantization can decrease the logic resource consumption of the convolutional kernel design by nearly 50% on a field-programmable gate array (FPGA). Without time-consuming retraining, the proposed static BFP quantization is able to quantize the precision to 8-bit mantissa with negligible accuracy loss. As different CNNs on our reconfigurable system require different hardware and software parameters to achieve optimal hardware performance and accuracy, we also propose an automatic tool for parameter optimization. Based on our hardware design and optimization, we demonstrate that the proposed accelerator can achieve 3.8-5.6 times higher energy efficiency than graphics processing unit (GPU) implementation. Comparing with the state-of-the-art FPGA-based accelerators, our design achieves higher generality and up to 1.4-2.2 times higher resource efficiency on both 2-D and 3-D CNNs.

Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding

In recent years, welding feedback control systems and weld quality estimation systems have been developed with the use of artificial intelligence to increase the quality consistency of robotic welding solutions. This paper introduces the utilization of an intelligent welding system (IWS) for feedback controlling the welding process. In this study, the GMAW process is controlled by a backpropagation neural network (NN). The feedback control of the welding process is controlled by the input parameters; root face and root gap, measured by a laser triangulation sensor. The NN is trained to adapt NN output parameters; wire feed and arc voltage override of the weld power source, in order to achieve consistent weld quality. The NN is trained offline with the specific parameter window in varying weld conditions, and the testing of the system is performed on separate specimens to evaluate the performance of the system. The butt-weld case is explained starting from the experimental setup to the training process of the IWS, optimization and operating principle. Furthermore, the method to create IWS for the welding process is explained. The results show that the developed IWS can adapt to the welding conditions of the seam and feedback control the welding process to achieve consistent weld quality outcomes. The method of using NN as a welding process parameter optimization tool was successful. The results of this paper indicate that an increased number of sensors could be applied to measure and control the welding process with the developed IWS.

A novel thermo-geometrical model for accurate keyhole porosity prediction in Laser Powder-Bed Fusion

AbstractWhen performing Laser Powder-Bed Fusion, undesired physical phenomena, such as balling, preballing and keyhole, must be avoided in order to achieve high-quality products. To date, keyhole-free process parameters can be identified either using demanding empirical methods or complex numerical simulations, while only a few analytical models can be found in literature for this purpose. In this work, state-of-the-art analytical models for predicting keyhole porosity were summarized and proved to be rather inaccurate because they are only based on thermodynamic principles, whereas they neglect the geometry and both the kinetics and kinematics of the keyhole cavity, which do also influence cavity collapse and porosity formation. Here an innovative physics-based semi-analytical model for predicting the formation of keyhole-related porosities was conceived, in which both thermodynamic and geometrical factors are taken into account. The proposed model was validated by performing single tracks experiments on Ti6Al4V according to a full factorial DoE on laser power and scanning speed. Produced samples were cross-sectioned and analyzed to evaluate keyhole porosity formation. The comparison between experimental data and model predictions confirmed the higher accuracy of the new model with respect to state of the art models. Besides improving the understanding of the keyhole phenomenon, the proposed model may provide a novel, effective and simple tool for fast process parameter optimization in industry.

Machine Learning Assists Bit Selection and Optimization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 21299, “Drilling in the Digital Age: Machine-Learning-Assisted Bit Selection and Optimization,” by Peter Batruny, SPE, Hafiz Zubir, and Pete Slagel, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. _ The project outlined in the complete paper describes machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. The study demonstrates how artificial neural networks (ANNs) can be used to learn from previous operations and influence planning decisions to improve bit performance. Introduction Multiple wells have been drilled in an onshore field in Iraq using different bit designs and with a variety of downhole conditions. To improve the rate of penetration (ROP) in a significant manner, a radical shift in how drill bits are selected, as well as a closer look at bit characteristics, is needed. This work aims to combine analytical and physical models to examine aspects of bit designs previously overlooked. Two mathematical models traditionally have been used for bit selection, the cost per foot (CPF) model and the mechanical specific energy (MSE) model. This work concludes that MSE is more effective than CPF in optimizing ROP and is a valid benchmark for comparing bits. MSE can be used to compare previous bit runs but is limited in predicting ROP for future bit runs. Equipment and Processes Data governance consists of data collection, processing, cleaning, and compilation. Data are collected from American Standard Code for Information Interchange logs in real time and then are processed by removing missing values. The data sets are split by individual bits, with each bit characterized by data compiled from multiple wells in which the bit was run. Throughout this paper, ROP will refer to instantaneous on-bottom ROP. Given the success that ANNs have had, and continue to have, in predicting ROP in the literature, they are used for this work. Once a model is established based on statistical and physical performance, it is used as a base model to run optimization analysis for each bit. This paper examines two methods of optimization, the first being sensitivity analysis based on average ROP. The second optimization method used in this study is a Monte Carlo-style optimization. Once optimal parameters are generated from the sensitivity analysis and Monte Carlo models, they are plotted against the modeled values of offset ROP. This acts as a further verification of the optimal parameters to ensure they are within range of actual historical parameters. The optimal values are then implemented in future wells.

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea

Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.

Modeling and global optimization of biodiesel synthesis using hybrid response surface methodology‐crow search algorithm: Case study of papaya seed waste oil utilization

AbstractBiodiesel is potential renewable and clean energy, which can be produced form wide range of waste materials. This study employs a hybrid response surface methodology (RSM) and crow search algorithm (CSA) as novel tool for global optimization of transesterification reaction parameters to maximize biodiesel synthesis from papaya seed‐derived waste oil. Catalyst (NaOH) dose, methanol to oil molar ratio (M:O), and reaction time were considered independent factors, while biodiesel yield was taken as a dependent variable. The experimentally produced biodiesel was characterized by gas chromatography–mass spectrometry analysis. The experiments were developed based on RSM with Box–Behnken design matrix, which was subsequently used for modeling, optimization and model validation. Initially, a quadratic regression model was developed following RSM technique, correlating the transesterification reaction parameters and biodiesel yield. Afterward, the CSA coupled with RSM approach was employed to assess the global optimization. A highest biodiesel yield of 99.48% was attained with a catalyst (NaOH) dose of 0.5 wt%, M:O of 8.5:1 at a reaction time of 40 min. The results acquired by RSM‐CSA were also compared with the results achieved by desirability function‐based optimization technique. Further, the optimal set for maximizing biodiesel yield was validated experimentally with an error margin of 2.0%. These observations indicate that the hybrid RSM‐CSA is an efficient and economic approach to optimize the process conditions for biodiesel production from alternative sources.

Modeling of the temperature field in nanosecond pulsed laser ablation of single crystalline diamond

In this paper, a comprehensive finite element (FE) simulation model of the heat transfer for laser ablation of a single crystalline diamond (SCD) with a scanning laser beam in Gaussian shape is developed. The model takes into account the material properties, the geometric structure, and the thermal boundary conditions. It is employed to study the dependence of the temperature distribution under varying laser machining parameters. The distribution characteristics of the temperature field, the temperature evolution, and the heat conduction on the diamond surface are obtained, analyzed, and discussed. The law describing the influence of the laser parameters on the temperature field on the diamond surface is established. After comparing the numerically estimated thermal penetration depth of the pulsed laser in the diamond with the experimentally ablated groove characterized by a scanning electron microscope (SEM), a white light interferometer (WLI) and the Raman spectroscopy analysis of the deposited metamorphic layer, it is found that the developed model shows an excellent predictive capability and provides a promising tool for the future optimization of the machining parameters.

Prediction of lack-of-fusion porosity in laser powder-bed fusion considering boundary conditions and sensitivity to laser power absorption

This paper proposes an analytical modeling method for the prediction of lack-of-fusion porosity of parts fabricated by laser powder-bed fusion (LPBF), with the consideration of boundary heat transfer and sensitivity to laser power absorption. The temperature distribution of the part was first predicted by an analytical thermal model, which consists of a linear heat source solution and a heat sink solution. The temperature increase due to laser power input was calculated by the point moving heat source solution. The temperature drop due to thermal conduction, convection, and radiation at part boundaries was calculated by the heat sink solution. The coefficient of laser power absorption was inversely obtained by comparing predicted molten pool widths with experimental measurements. The lack-of-fusion area was then calculated by plotting molten pool shapes of multi-tracks and multi-layers on a transverse cross-sectional area of the part. The powder bed porosity was calculated by an advancing front method with the consideration of powder size distribution and packing pattern. Finally, the lack-of-fusion porosity was obtained by multiplying the lack-of-fusion area with powder bed porosity. The predicted results were close to the measurements of Ti6Al4V in LPBF. The maximum deviation for porosity prediction is 5.91%. The presented model shows high computational efficiency without relying on iteration-based numerical method. The computational time for five consecutive layers is less than 100 s. The acceptable accuracy, short computational time, and the ability to consider complex boundary effects make the proposed method a good basis for future research and a promising tool for optimization of process parameters in LPBF of complex parts.

Computational Tool for Sizing and Optimization of Planimetric Geometric Parameters of Inland Navigation Channels and of Port Access in Brazil

The design of an access channel is based on regulations and recommendations from each country after considering natural and vessel-related criteria and characteristics, always focusing on navigation safety. This paper presents a set of criteria and technical parameters necessary for the planimetric dimensioning, in straight and curved sections, of navigation channels for inland waterways and port access channels, using a computational tool developed and called DimChannel (Channel Dimensioning Navigation), based on the recommendations of the World Water Transport Infrastructure Association (PIANC). The DimChannel was supported by the technical criteria present in recommendations of the PIANC, in which the characteristics of the vessels and waterways adopted in the standard are like the characteristics of Brazilian inland navigation. The software was validated through a case study in the lower Tapajós River, Amazon, Brazil. The results obtained showed that DimChannel is a tool of great importance for the planimetric dimensioning of navigation channels.

Climatic determinants of lowland rice development

AbstractAccurate modelling of plant development is the basis for any assessment of climate change impact on crop yields. Most rice models simulate development (phenology) based on temperature and photoperiod, but often the reliability of these models is reduced beyond the environment they were calibrated for. In our study, we tested the effects of relative air humidity and solar radiation on leaf appearance rate in greenhouse experiments and analysed data sets from field studies conducted in two extremely different rice‐growing environments in Nepal and Senegal. We also analysed environmental effects on duration to flowering of one popular IRRI material (IR64) for eight different sites covering the entire temperature range where rice is widely cultivated. Both low relative air humidity and low solar radiation significantly decreased leaf appearance rate. Mean air temperature explained 81% of the variation in duration to flowering across sites, which was furthermore significantly influenced by relative air humidity. Across all sites, a simple linear regression approach including mean air temperature and mean relative humidity in the calculation of duration to flowering led to a root mean square error (RMSE) of 10 days, which was slightly lower than the RMSE of 11 days achieved with an automated calibration tool for parameter optimization of cardinal temperatures and photoperiod sensitivity. Parameter optimization for individual sites led to a much smaller prediction error, but also to large differences in cardinal temperatures between sites, mainly lower optimum temperatures for the cooler sites. To increase the predictive power of phenological models outside their calibration range and especially in climate change scenarios, a more mechanistic modelling approach is needed. A starting point could be including relative air humidity and radiation in the simulation procedure of crop development, and presumably, a closer link between growth and development procedures could help to increase the robustness of phenological models.

Estimating crop genetic parameters for DSSAT with modified PEST software

Abstract Quickly determining accurate crop genetic parameters for crop model applications can be difficult. In this study, we coupled the independent automatic parameter optimization tool PEST (Parameter ESTimation) with the crop growth model of DSSAT (Decision Support System for Agrotechnology Transfer) using the R programming language. A new DSSAT-PEST package was developed to perform automatic optimization of the crop genetic parameters. In addition, the PEST tool was modified to reduce problems associated with local optima and model runtime. The DSSAT-PEST package was used to estimate the genetic coefficients for five crops (i.e., maize (Zea mays L.), soybean (Glycine max L. Merrill), wheat (Triticum aestivum L.), rice (Oryza sativa L.), and cotton (Gossypium hirsutum L.)) based on existing experiments in the DSSAT database. Three parameter optimization methods were compared based on their efficiency and accuracy for estimating crop genetic parameters: 1) the traditional trial-and-error method (default crop genetic parameters in the DSSAT database); 2) DSSAT-GLUE (general likelihood uncertainty estimation, an existing parameter estimation package in DSSAT), and 3) DSSAT-PEST. The DSSAT-PEST optimization method produced reasonably accurate optimization results and improved optimization efficiency compared with the other two methods. For example, the average absolute relative error (AREs) between relevant field observations and model simulations obtained with DSSAT-PEST were 12 %, 7 %, 18 %, 4 %, and 19 % for the five crops, respectively, which were similar to or better than the results with DSSAT-GLUE and the default method. Additionally, average runtime for DSSAT-PEST was about 65 % of the runtime for DSSAT-GLUE. In general, the DSSAT-PEST package performed similarly to or better than the traditional trial-and-error method and DSSAT-GLUE in terms of both optimization efficiency and accuracy, which should promote wider application of the DSSAT model in agricultural and environmental research.

Shallow and deep neural network training by water wave optimization

It is well known that the performance of artificial neural networks (ANNs) is significantly affected by their structure design and parameter selection, for which traditional training methods have drawbacks such as long training times, over-fitting, and premature convergence. Evolutionary algorithms (EAs) have provided an effective tool for ANN parameter optimization. However, simultaneously optimizing ANN structures and parameters remains a difficult problem. In this study, we adapt water wave optimization (WWO), a relatively new EA, for optimizing both the parameters and structures of ANNs, including classical shallow ANNs and deep neural networks (DNNs). We use a variable-dimensional solution encoding to represent both the structure and parameters of an ANN, and adapt WWO propagation, refraction, and breaking operators to efficiently evolve variable-dimensional solutions to solve the complex network optimization problems. Computational experiments on a variety of benchmark datasets show that the WWO algorithm achieves a very competitive performance compared to other popular gradient-based algorithms and EAs.

Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification.

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

Experimental design as a tool for parameter optimization of photoelectrocatalytic degradation of a textile dye

Although several factors are found to influence the efficiency of photoelectrocatalytic oxidation, univariate optimization may not provide some relevant information, such as the existence of interaction between the factors. This article describes the optimization of photoelectrocatalytic parameters by response surface methodology (RSM) using a two-level full factorial composite design with center point. The factors investigated included pH, bias potential and counter electrode material (C.E.) employed in the photoelectrocatalytic degradation of 50 mg L−1 Acid Red 151 (AR151) azo dye (taken as a model textile dye) in 0.01 mol L−1 Na2SO4 electrolyte, using boron-doped TiO2 nanotube (B-TNT) photoanode under UV/Vis Hg lamp irradiation. The responses evaluated were total organic carbon removal and decolorization. The results showed that all the factors exerted a significant effect, among which the pH was the variable with the greatest impact on the PEC treatment. Although less significant, interactions between the variables, such as pH and the counter electrode, were observed for both responses. The best conditions for the photoelectrocatalytic degradation of AR151 dye were pH 2.0 and potential of 2.0 V using graphite as counter electrode. Photoelectrocatalytic treatment of 50 mg L−1 of AR151 dye reached total decolorization in 30 min and almost total mineralization after 90 min. The findings show that the use of RSM to optimize degradation conditions helps to save time and chemicals, in addition to contributing toward a better understanding of the factors that affect photoelectrocatalytic performance.

“Optimization of Transesterification Reaction Parameters For Fish Oil Biodiesel Production: A Response Surface Methodology Approach”

The Fish oil was used to produce biodiesel by transesterification reaction with methanol as a solvent in presence of a mixture of sodium hydroxide & di-sodium orthophosphate as catalyst concentrations (mixed base catalyst). The optimization of transesterification reaction parameters were done using Response Surface Methodology (RSM) tool. Three process variables were assessed viz., Methanol to Oil Ratio (MOR), Reaction Time (RT) & Reaction Temperature (RTE). To get maximum yield of fish oil biodiesel (FOB), 20 experiments were designed and conducted. A yield of 94.6% FOB was obtained during the transesterification process carried out at the optimized reaction parameters viz., 7.5:1 MOR, 45 minutes RT & 70°C RTE. Statistical analysis of the predicted model by RSM revealed a good agreement between the observed and predicted values. Present research is successful in effectively using RSM tool for optimization of transesterification reaction parameters.