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Auswahl der wissenschaftlichen Literatur zum Thema „Mikrofiltrace“
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Zeitschriftenartikel zum Thema "Mikrofiltrace"
Shalahuddin, Iqbal, und Yusuf Wibisono. „Mekanisme Fouling pada Membran Mikrofiltrasi Mode Aliran Searah dan Silang“. Jurnal Rekayasa Proses 13, Nr. 1 (01.07.2019): 6. http://dx.doi.org/10.22146/jrekpros.40458.
Der volle Inhalt der QuelleBUGAN, S. G., D. ŠMOGROVIČOVÁ, Z. DÖMÉNY, J. STOPKA und Š. SCHLOSSER. „Application of the "crossflow" microfiltration in the brewing industry.“ Kvasny Prumysl 47, Nr. 4 (01.04.2001): 97–101. http://dx.doi.org/10.18832/kp2001008.
Der volle Inhalt der QuelleAryanti, N., und H. Susanto. „Pengolahan Air Gambut Dengan Kombinasi Proses Flokulasi dan Mikrofiltrasi“. REAKTOR 8, Nr. 1 (19.06.2017): 43. http://dx.doi.org/10.14710/reaktor.8.1.43-47.
Der volle Inhalt der QuelleRahayu, Iman. „PEMBUATAN DAN KARAKTERISASI MEMBRAN KERAMIK DENGAN VARIASI TEPUNG BERAS SEBAGAI ADITIF UNTUK PROSES MIKROFILTRASI“. Jurnal Sains dan Terapan Kimia 11, Nr. 2 (03.10.2017): 52. http://dx.doi.org/10.20527/jstk.v11i2.4035.
Der volle Inhalt der QuelleWibisono, Yusuf, Ashried Faradilla, Panggulu Ahmad Utoro, Agung Sukoyo, Ni'matul Izza und Shinta Rosalia Dewi. „Anti-biofoulan Alami Moringa oleifera Sebagai Bahan Pengisi Membran Mixed Matrix Selulosa Asetat untuk Klarifikasi Jus Buah“. Jurnal Rekayasa Kimia & Lingkungan 13, Nr. 2 (17.08.2018): 100–109. http://dx.doi.org/10.23955/rkl.v13i2.11053.
Der volle Inhalt der QuelleAspiyanto, Aspiyanto, Agustine Susilowati und Yati Maryati. „SEPARATION OF SAVORY FRACTION FROM AUTOLYSATE OF KIDNEY BEAN (Phaseolus vulgaris L.) FERMENTED BY Rhizopus sp-PL19 THROUGH CROSS-FLOW MICROFILTRATION (CFMF) MEMBRANE MODULE“. Jurnal Kimia Terapan Indonesia 16, Nr. 1 (10.06.2014): 39–48. http://dx.doi.org/10.14203/jkti.v16i1.7.
Der volle Inhalt der QuelleMaulina, Wenny. „KAJIAN MEMBRAN KOMPOSIT NILON-ARANG MELALUI KARAKTERISASI FTIR DAN SEM“. Jurnal Pendidikan Fisika dan Keilmuan (JPFK) 2, Nr. 1 (20.03.2016): 56. http://dx.doi.org/10.25273/jpfk.v2i1.25.
Der volle Inhalt der QuelleDoddy, Putu, Ester Susanti und Debby Mariana. „Studi penggunaan membran berslot untuk memproduksi emulsi minyak/air“. Jurnal Teknik Kimia Indonesia 9, Nr. 1 (02.10.2018): 19. http://dx.doi.org/10.5614/jtki.2010.9.1.3.
Der volle Inhalt der QuelleKurniawan, Ian, und Pra Dian Mariadi. „REVIEW : PROFIL HYBRID MEMBRANE DALAM PROSES REDUKSI AIR LIMBAH“. JURNAL KONVERSI 5, Nr. 1 (10.04.2015): 1. http://dx.doi.org/10.24853/konversi.5.1.1-10.
Der volle Inhalt der QuelleDita Auline Saragih, Nurul Qomariah und Abdullah Saleh. „Pembuatan membran komposit nilon-karbon aktif dengan variasi suhu dan waktu pengadukan“. Jurnal Teknik Kimia 24, Nr. 3 (01.11.2018): 89–93. http://dx.doi.org/10.36706/jtk.v24i3.29.
Der volle Inhalt der QuelleDissertationen zum Thema "Mikrofiltrace"
Bojana, Ikonić. „Modelovanje i optimizacija procesa mikrofiltracije suspenzija pšeničnog skroba“. Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2011. https://www.cris.uns.ac.rs/record.jsf?recordId=76896&source=NDLTD&language=en.
Der volle Inhalt der QuelleThe aim of this study was to investigate the effect of process parameters (transmembrane pressure, flow rate and suspension concentration) on the permeate flux in the system with and without the presence of static mixer. Microfiltration of wheat starch suspensions was performed in recirculation and concentration mode using ceramic membranes with different pore size (200 nm and 500 nm). Response surface methodology was applied for modeling cross-flow microfiltration of starch suspensions. During investigation of starch suspension microfiltration process on membranes with different pore size diameter (200 and 500 nm) it was observed that with increasing pore size the permeate flux declined. In the experimental range of process parameters, flux increase had values between 25% and 50% in recirculation mode, while in concentration mode this improvement was in range between 20% and 80%. The increase in flux that occurs by placing a static mixer in the membrane channel was caused by the establishment of turbulent flow conditions and the characteristic flow of fluid along the membrane channel, which is a consequence of the characteristic geometry of Kenics static mixer. Both in recirculation and concentration mode, the reduction of specific energy consumption depends almost exclusively on the value of the suspension flow rate. Specific energy consumption increased rapidly with increasing flow rate in the presence of static mixers and flux improvement is not high enough to compensate the loss of hydraulic dissipated power. The flow rate in the range from 80 to 100 L/h provided positive values of the reduction of specific energy consumption and the use of static mixers was justified from the economical point of view. Optimization of experimental conditions was done by a procedure of simultaneous maximization of permeate flux in systems with static mixers and reduction of specific energy consumption. Optimal conditions of the wheat starch suspension microfiltration in recirculation mode indicate that the process should be conducted at the maximum value of transmembrane pressure of 0.9 bar, flow rates from 85 to 100 L/h and concentration of 5 to 6 g/L. Optimal conditions of the wheat starch suspension microfiltration in concentration mode indicate that the process should be conducted when the value of transmembrane pressure from 0.85 to 0.9 bar, flow rates from 85 to 100 L/h and concentration of 5 to 7 g/L. Apart from investigations in laboratory conditions, the aim of this study was to examine the influence of process parameters on the starch suspensions microfiltration in the pilot plant (one channel and multichannel membrane with pore diameter 200 nm) and wider range of transmembrane pressure and suspension flow rate on the mentioned responses in concentration mode.
Nevenka, Nikolić. „Modelovanje mikrofiltracije kultivacionih tečnosti primenom koncepta veštačkih neuronskih mreža“. Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2020. https://www.cris.uns.ac.rs/record.jsf?recordId=114867&source=NDLTD&language=en.
Der volle Inhalt der QuelleFocus of this doctoral dissertation is to developa model based on the artificial neural networksconcept for predicting and designing cultivationbroth microfiltration by examining thefeasibility of this concept for modelingpermeate flux under different microfiltrationconditions, in systems with and withouthydrodynamic im provem ent methods, as wellthe development of a model that will combinethe experimental results in order to obtain asingle neural network to simulate all methods offlux improvement. An additional goal is thedevelopment of a model in quasi steady state interm so fadequacy of flux enhancement methodsapplication, which will be checked from theenergy point of view.Experimental tests included the developmentand validation of ten different models оf neuralnetworks in which the independent inputvariables and their ranges (transmembranepressure, suspension flow and air flow) weredetermined by Box-Behnken's experimentalplan with added microfiltration parameters timeand temperature, varied depending on theconditions of the microfiltration procedure. Incontrast, for the development оf a dynamicmodel as a dependent variable, the decrease inpermeate flux with time was considered, whilefor the development of a model for evaluatingthe efficiency оf applied permeate fluxim provement methods, flux and specific energyconsumption in quasi steady state conditionswere considered.Normalization of experimental data avoided alarge difference in specific weight coefficients of individual input variables and prevented thedanger that these variables show a greaterimpact than they have in reality, and balancingthe effects of uncontrolled factors on the outputvariable was performed by randomization on thetraining group (70% o f data), a validation group(15% of data) and a testing group (15% of data).Non-stationarities affecting the efficiency of thetraining algorithm and neural networkarchitecture were avoided by testing the modelwith five diferent training algorithms(Levenberg-M arquardt training algorithm(trainlm), Bayesian regularization (trainbr),resilient backpropagation algorithm (trainrp),scaled conjugate gradient method (trainscg) anda one-step secant m ethod (trainoss)) and twosigmoid activation functions in the hidden layer(logistic and hyperbolic tangent), while a linearactivation function was used in the output layer.All models are optimized by applying the trialand error method with the basic goal of havingthe simplest possible network, ie a network witha minimum num ber o f hidden neurons thatshows the best ability to generalize.Determ ination coefficient (R2) and mean squareerror (MSE) were examined as indicators ofgeneralization level and neural network trainingperformance parameters, and correlationcoefficient (r) was selected as an additionalparam eter o f adequacy оf fitting the value ofdetermined and neural network estimatedpermeate flux.The best ability to generalize and predict wasshown by a model of a neural network trainedby the Levenberg-M arquardt algorithm. Theoptimal num ber of neurons in the hidden layerranged from 7 to 13, which indicates asignificant complexity of the mechanisms thataffect the permeate flux, as assessed by thehypothesis of this doctoral dissertation.Absolute relative error analysis showed verygood prediction as in the range of 81% to 100 %of the data had an error of less than 10 %, andthe coefficient of determination in the range of0.98091 to 0.99976 indicates that the networkcannot explain less than 2 % variation in thesystem. The values оf the correlation coefficientrange from 0.99041 to 0.99988 suggests a goodlinear correlation between the experimental dataand the data predicted by the neural network. In addition to the application of the concept of datafitting, the relative importance of input variableswas also investigated by applying the Garsonequation. Comparative analysis of the obtainedsimulation results on experimental data thatwere not presented to the neural networkconfirmed the generalization capacity of theneural network model.
Uhlířová, Marcela. „Využití membrán pro zpracování odpadních vod ze zemědělství“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443232.
Der volle Inhalt der QuelleAleksandar, Jokić. „Modelovanje "cross-flow" mikrofiltracije suspenzija kvasca primenom koncepta neuronskih mreža i postupka odzivne površine“. Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2010. https://www.cris.uns.ac.rs/record.jsf?recordId=77402&source=NDLTD&language=en.
Der volle Inhalt der QuelleThe aim of this work was to investigatepossibilities of applying neural network andresponse surface methodology for modeling crossflowmicrofiltration of yeast suspensions. Anotheraim was to investigate the improvement of processusing Kenics static mixer as turbulence promoter.Experimental work was performed on 200, 450 and800 nm tubular ceramic membranes. The use ofstatic mixer was also examined from an energeticpoint of view not only its influence on permeateflux. All experiments were done in recirculation andconcentration mode.The results clearly show that theimprovement of cross-flow microfiltration of yeastsuspensions performances can be done with staticmixer without any additional equipment. Inexperimental work, flux increase had valuesbetween 89.32% and 258.86% for recirculation offeed suspension depending on experimental valuesof selected variables while in concentration modethis improvement was in range between 100% and540% for the same range of experimental variables.Neural networks had excellent predictivecapabilities for this kind of process. Besidesexamination of predictive capabilities of neuralnetworks influence of each variable was examinedby applying Garson equation and connectionweights method. Results of this analysis were infairly good agreement with regression analysis.For more detailed analysis of variables influence onthe selected responses response surfacemethodology was implemented. First step was toinvestigate the influence of membrane pore size onthe process of microfiltration. The results suggestedthat the best way to conduct microfiltration of yeastsuspensions is by using the membrane with meanpore size of 200 nm, because bigger mean pore sizecan lead to more prominent internal fouling thatcauses smaller flux values.Further investigations of microfiltrationprocess were done in order to investigate influencesof variables as well as their interactions and it wasdone for the membrane with pore size of 200 nm.Results for this membrane considering regressionanalysis were considerably better compared withresults obtained for modeling all three membranes.From the energetic point of view it was concludedthat it is optimal to use moderate feed flows toachieve best results with implementation of staticmixer.As the final goal of response surfacemethodology optimization of process variables wasdone by applying desirability function approach.Optimal values of process variables forrecirculation of feed suspension weretrasmembrane pressure 0.2 bar, concentration 7.54g/l and feed flow 108.52 l/h for maximal values ofspecific energy reduction. On the other side forconcentration of feed suspension these variableshad values of 1 bar, 7.50 g/l and 176 l/h
Bücher zum Thema "Mikrofiltrace"
Ultrafiltracja i mikrofiltracja w uzdatnianiu wód do celów komunalnych. Gliwice: Wydawn. Politechniki Śląskiej, 2000.
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