Palm Oil Mill Effluent Treatment Through Combined Process Adsorption and Membrane Filtration

Abstract: The growth in palm oil production also leads to an Increase in the production of palm oil mill effluent (POME). Nowadays, POME was treated using an open lagoon but this method is ineffectiveness in complying with the standards for water disposal. Therefore, efficient and cohesive treatment system is highly desired to ensure the final discharge of the treated water meets the effluent discharge standards. Initially, the POME was treated through adsorption, followed by UF membranes roomates were intended to reduce COD, TSS and turbidity up to 88%, 99%, and 98%, while the final treatment of RO membranes can reduce BOD, COD and color up to 92%, 98% and 99%. To determine the optimum condition of the RO membrane, response surface methodology (RSM) was used. The results showed there was correlation between all key variables. POME concentration, trans-membrane pressure, pH and time would give significant effects in reducing the parameters in POME treatment with the optimum condition of 15.77% for POME concentration, 3.73 for pH, 0.5 bar trans-membrane pressure and 5 hours for filtration time. To predict COD removal, the results were analyzed by applying the artificial neural network (ANN) to derive a mathematical model.Keywords: POME, Adsorption, Membrane filtration, COD, RSM, ANNAbstrak (Indonesian): Pertumbuhan produksi minyak kelapa sawit juga meningkatkan produksi air buangan minyak kelapa sawit (POME). Sekarang ini, POME diolah menggunakan kolam terbuka tetapi metode ini tidak efisien dan tidak memenuhi persyaratan standar air buangan industri. Oleh karena itu, diperlukan suatu sistem pengolahan yang efektif dan terpadu untuk memastikan air buangan pada tahap akhir telah memenuhi standar air buangan.  Pada awalnya, POME diolah melalui adsorpsi dan diikuti oleh membran UF dengan tujuan untuk mengurangi kadar COD, TSS dan kekeruhan hingga 88%, 99% dan 98%, masing-masing.  Sementara itu, pada proses akhir digunakan membran RO yang berhasil menurunkan kadar BOD, COD dan warna hingga 92%, 98%, dan 99%, masing-masing.  Untuk menentukan kondisi optimum dari membran RO digunakan metode respon permukaan (RSM).  Hasil memperlihatkan ada korelasi antara semua variabel. Konsentrasi POME, tekanan trans membran, pH aturan dan waktu memberikan pengaruh penting dalam pengurangan parameter pada pengolahan POME, dengan kondisi operasi optimum sebagai berikut: 15,77% bagi konsentrasi, 3,73 bagi pH, 0,5 bar bagi tekanan trans membran, dan 5 jam waktu filtrasi.  Untuk memprediksi penghilangan COD, hasil diperiksa menggunakan metode jaringan saraf tiruan (ANN). Hal ini bertujuan untuk mendapatkan suatu model matematika.Kata kunci: POME, Adsorpsi, Membran filtrasi, COD, RSM, AN

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

© 2016 Taylor & FrancisWell-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Modelado del proceso de ultrafiltración en un biorreactor de membranas utilizando redes neuronales

En este trabajo de investigación se aborda la predicción de la presión transmembranal en un biorreactor de membranas con membranas planas sumergidas susceptibles a contralavados. Este proceso de predicción se realiza utilizando una Red Neuronal Perceptrón Multicapa de tres capas, lo cual permite reducir las unidades y el tiempo de procesamiento. La capacidad de interpolación y extrapolación de las estructuras de las redes neuronales artificiales propuestas utilizando diferentes conjuntos de variables de entrada y datos de operación, son evaluadas analizando los coeficientes de correlación obtenidos para cada topología.Carreño Martínez, YM. (2009). Modelado del proceso de ultrafiltración en un biorreactor de membranas utilizando redes neuronales. http://hdl.handle.net/10251/11270Archivo delegad

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Optimizacija mikrotalasne ekstrakcije polifenolnih jedinjenja iz ploda aronije (Aronia melanocarpa L.)

Aronia (Aronia melanocarpa) is one of the richest plant sources of polyphenols, mainly anthocyanins that provide a high antioxidant activity. In order to find extraction technique and conditions that will allow obtaining chokeberry extract with the maximum content of polyphenolic compounds, the microwave assisted extraction under different conditions (microwave power, ethanol concentration and extraction time) could be applied. Statistical methods (response surface methodology and artificial neural network with genetic algorithm) estimate the individual and combned effect of process parameters and proposed the optimal and most cost-effective extraction conditions which provide the highest possible content of polyphenolic compounds and antioxidant activity. The formulation of cookies based on wheat and buckwheat flour with the addition of fresh chokeberry fruits, juice or aronia extract obtained under optimum conditions leads to an improvement of conventional recipes and production of functional bakery products