Optimization of A2O BNR processes using ASM and EAWAG Bio-P models: model formulation

2011 ◽  
Vol 46 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Walid El Shorbagy ◽  
Nawras Nabil ◽  
Ronald L. Droste
Keyword(s):  
Activated Sludge ◽  
Operating Cost ◽  
Model Performance ◽  
Problem Definition ◽  
Biological Nutrient Removal ◽  
Total Annual Cost ◽  
Model Parameters ◽  
Model Formulation ◽  
Highly Nonlinear ◽  
Generalized Reduced Gradient

This study is an extended and comprehensive analysis to accomplish optimal sizing for a biological nutrient removal (BNR) system with an A2O BNR activated sludge process using activated sludge models (ASM) kinetic models. A highly nonlinear activated sludge model combined with the EAWAG Bio-P module is formulated and optimized using a generalized reduced gradient solver. Primary and final clarifications are included with the A2O biotreatment scheme along with oxygen-supplying units. This paper includes a detailed description of model formulation, problem definition and discussion of optimal design in terms of capital (CAPEX) and operating (OPEX) cost estimates. The optimization problem is formulated and solved using typical cost factors and operating/design constraints applied to a typical illustrative system treating medium-strength wastewater. Results indicated that maintenance and sludge disposal expenditures represent more than 50% of the total annual cost and 80% of the annual running operating cost. Another major finding was that a primary clarifier is found to be cost ineffective in the A2O BNR process. Sensitivity of the optimal solutions and model performance to varying inflow conditions and to other effluent limits and model parameters will be discussed in another paper.

scholarly journals Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

2019 ◽  
Vol 20 (13) ◽  
pp. 3216 ◽  
Author(s):  
Yu ◽  
Li ◽  
Li ◽  
Li ◽  
Li ◽  
...  
Keyword(s):  
Phenomenological Models ◽  
Optimization Problem ◽  
Fitness Function ◽  
Model Performance ◽  
Friction Model ◽  
Shaking Table ◽  
Model Parameters ◽  
Magnetorheological Elastomer ◽  
Highly Nonlinear ◽  
Hysteresis Response

Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

Are standard wastewater treatment plant design methods suitable for any municipal wastewater?

2012 ◽  
Vol 66 (2) ◽  
pp. 328-335 ◽  
Author(s):  
G. Insel ◽  
B. Güder ◽  
G. Güneş ◽  
E. Ubay Cokgor
Keyword(s):  
Activated Sludge ◽  
Volatile Fatty Acids ◽  
Municipal Wastewater ◽  
Treatment Plant ◽  
Design Methods ◽  
Biological Nutrient Removal ◽  
Model Parameters ◽  
Plant Design ◽  
Biological Phosphorus Removal ◽  
Operational Parameters

The design and operational parameters of an activated sludge system were analyzed treating the municipal wastewaters in Istanbul. The design methods of ATV131, Metcalf & Eddy together with model simulations were compared with actual plant operational data. The activated sludge model parameters were determined using 3-month dynamic data for the biological nutrient removal plant. The ATV131 method yielded closer sludge production, total oxygen requirement and effluent nitrogen levels to the real plant after adopting correct influent chemical oxygen demand (COD) fractionation. The enhanced biological phosphorus removal (EBPR) could not easily be predicted with ATV131 method due to low volatile fatty acids (VFA) potential.

Pembuatan Pilot Project Pengolahan Air Limbah Industri Rambut Palsu dengan Sistem Lumpur Aktif

2016 ◽  
Vol 8 (15) ◽  
pp. 37-47
Author(s):  
Sri Moertinah ◽  
Misbachul Moenir
Keyword(s):  
Activated Sludge ◽  
Biological Treatment ◽  
Operating Cost ◽  
Pilot Project ◽  
Labor Costs ◽  
Industrial Waste Water ◽  
Microbial Adaptation ◽  
Central Java ◽  
Electricity Costs

This study aims to create a pilot project for wastewater treatment wig industry with biological activated sludge technology to applied in the industry. Design criteria for the pilot project are the influent COD ≤ 900 mg/l, MLSS = 3,000 mg/l, 30-hours residence time. DO ≥ 2 mg/l and flow 10 m3/day. Implementation of a pilot project initiated by seeding aerobic microbes and microbial adaptation to proceed with wastewater to be treated. The trial results showed that the pilot project % COD reduction ranged from 73.2% - 91% and the result is not much different from the results of laboratory-scale research about 89.7% and the quality  of the effluent is already fullfill the standard of industrial waste water wig required by the Central Java Provincial Regulation No. 5 of 2012. The calculation of operating cost of activated sludge biological treatment which includes labor costs, electricity costs, equipment maintenance costs, expenses and other nutrients obtained the price of  Rp. 2972/m3 or Rp. 742.99/wig.ABSTRAKPenelitian ini bertujuan untuk membuat pilot project pengolahan air limbah industri rambut palsu dengan sistem lumpur aktif yang diterapkan di industri. Kriteria desain pilot project tersebut adalah COD influen ≤ 900 mg/l, MLSS = 3.000 mg/l, waktu tinggal 30 jam DO≥2 mg/l  dan debit air limbah 10 m3/hari. Pelaksanaan pilot project dimulai dengan seeding mikroba aerob dan dilanjutkan dengan adaptasi mikroba dengan air limbah yang akan diolah. Hasil uji coba pilot project menunjukkan bahwa % penurunan COD berkisar antara 73,2% - 91% dan hasil ini tidak berbeda jauh dengan hasil penelitian skala laboratorium sekitar 89,7% dan kualitas air limbah hasil pengolahan sudah memenuhi baku mutu air limbah industri rambut palsu yang dipersyaratkan oleh Peraturan Daerah Provinsi Jawa Tengah No 5 tahun 2012. Dari hasil perhitungan biaya operasional pengolahan biologis lumpur aktif yang meliputi biaya tenaga kerja, biaya listrik, biaya perawatan peralatan, biaya nutrien dan lainnya diperoleh harga sebesar Rp. 2972/m3  atau Rp. 742,99/wig.   Kata kunci : air limbah industri rambut palsu, pilot project, sistem lumpur aktif

Model based evaluation of plant improvement strategies for biological nutrient removal

1999 ◽  
Vol 39 (4) ◽  
pp. 45-53 ◽  
Author(s):  
H. M. van Veldhuizen ◽  
M. C. M. van Loosdrecht ◽  
F. A. Brandse
Keyword(s):  
Activated Sludge ◽  
Wastewater Treatment Plants ◽  
P Uptake ◽  
Biological Nutrient Removal ◽  
Adequate Description ◽  
Control Scheme ◽  
Detailed Simulation ◽  
High Fraction ◽  
Biokinetic Parameters ◽  
P Removal

An activated sludge model for biological N- and P-removal was developed, which describes anoxic and aerobic P-uptake based on bacterial metabolism. This model was tested in practice on two wastewater treatment plants, which are BCFS®-processes, which contain activated sludge with a high fraction of denitrifying P-removing bacteria (DPB's). The model appeared to be able to give an adequate description of the performance of these treatment plants under different conditions. If the process parameters are well defined almost no calibration of the biokinetic parameters was necessary. In the simulation of Dalfsen wwtp, which has a complex control scheme, it was possible to give an adequate simulation of the control actions and the concentration profiles in a rather simple way, showing that detailed simulation of these controllers was not necessary. With the calibrated model it was possible to analyse bottlenecks and give suggestions for upgrading of the concerned treatments plants. The simulation results were used in decisions on investments.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

Development of an Inverse Plume Model for Mass Eruption Rate in Unsteady Conditions

2021 ◽  
Author(s):  
Stephen A Solovitz
Keyword(s):  
Laboratory Data ◽  
Model Performance ◽  
Volcanic Eruptions ◽  
Reynolds Numbers ◽  
Model Parameters ◽  
Plume Height ◽  
Eruption Rate ◽  
Mass Eruption Rate ◽  
Downstream Effects ◽  
Order Of Magnitude

Abstract Following volcanic eruptions, forecasters need accurate estimates of mass eruption rate (MER) to appropriately predict the downstream effects. Most analyses use simple correlations or models based on large eruptions at steady conditions, even though many volcanoes feature significant unsteadiness. To address this, a superposition model is developed based on a technique used for spray injection applications, which predicts plume height as a function of the time-varying exit velocity. This model can be inverted, providing estimates of MER using field observations of a plume. The model parameters are optimized using laboratory data for plumes with physically-relevant exit profiles and Reynolds numbers, resulting in predictions that agree to within 10% of measured exit velocities. The model performance is examined using a historic eruption from Stromboli with well-documented unsteadiness, again providing MER estimates of the correct order of magnitude. This method can provide a rapid alternative for real-time forecasting of small, unsteady eruptions.

scholarly journals Incremental model breakdown to assess the multi-hypotheses problem

2018 ◽  
Vol 22 (8) ◽  
pp. 4565-4581 ◽  
Author(s):  
Florian U. Jehn ◽  
Lutz Breuer ◽  
Tobias Houska ◽  
Konrad Bestian ◽  
Philipp Kraft
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Complex Model ◽  
Model Parameters ◽  
Objective Functions ◽  
Final Model ◽  
Incremental Model ◽  
Model Efficiency ◽  
Improved Model ◽  
Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation

1993 ◽  
Vol 28 (11-12) ◽  
pp. 163-171 ◽  
Author(s):  
Weibo (Weber) Yuan ◽  
David Okrent ◽  
Michael K. Stenstrom
Keyword(s):  
Activated Sludge ◽  
High Purity ◽  
Model Calibration ◽  
Process Model ◽  
Model Parameters ◽  
Complex Method ◽  
Activated Sludge Process ◽  
Activated Sludge Model ◽  
Kinetic Coefficients ◽  
Calibration Algorithm

A model calibration algorithm is developed for the high-purity oxygen activated sludge process (HPO-ASP). The algorithm is evaluated under different conditions to determine the effect of the following factors on the performance of the algorithm: data quality, number of observations, and number of parameters to be estimated. The process model used in this investigation is the first HPO-ASP model based upon the IAWQ (formerly IAWPRC) Activated Sludge Model No. 1. The objective function is formulated as a relative least-squares function and the non-linear, constrained minimization problem is solved by the Complex method. The stoichiometric and kinetic coefficients of the IAWQ activated sludge model are the parameters focused on in this investigation. Observations used are generated numerically but are made close to the observations from a full-scale high-purity oxygen treatment plant. The calibration algorithm is capable of correctly estimating model parameters even if the observations are severely noise-corrupted. The accuracy of estimation deteriorates gradually with the increase of observation errors. The accuracy of calibration improves when the number of observations (n) increases, but the improvement becomes insignificant when n>96. It is also found that there exists an optimal number of parameters that can be rigorously estimated from a given set of information/data. A sensitivity analysis is conducted to determine what parameters to estimate and to evaluate the potential benefits resulted from collecting additional measurements.

Experiences with computer simulation at two large wastewater treatment plants in northern Poland

2002 ◽  
Vol 45 (6) ◽  
pp. 209-218 ◽  
Author(s):  
J. Makinia ◽  
M. Swinarski ◽  
E. Dobiegala
Keyword(s):  
Computer Simulation ◽  
Wastewater Treatment ◽  
Nutrient Removal ◽  
Wastewater Treatment Plants ◽  
Biological Nutrient Removal ◽  
Model Parameters ◽  
Total N ◽  
Northern Poland ◽  
Model Predictions ◽  
Steady State Simulation

Mathematical modelling and computer simulation have became a useful tool in evaluating the operation of wastewater treatment plants (WWTPs) in terms of nutrient removal capability. In this study, steady-state simulation results for two large biological nutrient removal WWTPs are presented. The plants are located in two neighbouring cities Gdansk and Gdynia in northern Poland. Simulations were performed using a pre-compiled model and layouts (MUCT and Johannesburg processes) implemented in the GPS-X simulation package. The monthly average values of conventional parameters, such as COD, Total Suspended Solids, total N, N-NH4+, P-PO4− were used as input data. The measured effluent concentrations of COD, N-NH4+, N-NO3− and P-PO4− as well as reactor MLSS were compared with model predictions. During calibration, performed from the process engineering perspective, default values of only five model parameters were changed. The opportunities for further applications of such models in municipal WWTPs are discussed.

scholarly journals Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

2014 ◽  
Vol 14 (23) ◽  
pp. 32233-32323 ◽  
Author(s):  
M. Bocquet ◽  
H. Elbern ◽  
H. Eskes ◽  
M. Hirtl ◽  
R. Žabkar ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Hybrid Methods ◽  
Model Performance ◽  
Chemical Data ◽  
Current Status ◽  
Model Parameters ◽  
Chemical Transformations ◽  
Future Prospects ◽  
Main Challenge

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

Sign in / Sign up

Export Citation Format

Share Document