Model Identification, Parameter Estimation, and Dynamic Flux Analysis of E. coli Central Metabolism

In this work are applied three global optimisation algorithms for adaptation of the mathematical model of the central metabolism of Escherichia coli to data obtained in the experiment with response to glucose impulse. Applied is the adaptive simplex method by Nelder-Mead, evolutionary algorithms of differential evolution, and simulated annealing. The original model has been modified by the following steps: closure of Entner- -Doudoroff pathway with pyruvate balance, introduction of phosphoenolpyruavate carboxylase and carboxykinase reactions in the balance of phosphoenolypyravate, account for loss of pyruvate in biomass synthesis, change in kinetic rate expressions for several enzymes, and partial re-estimation of the kinetic parameters by the global optimisation algorithms. The modified model correctly predicts observed oscillatory response to glucose impulse in concentrations of pyruvate and D-ribose-5-phosphate. To discern metabolic control, evaluated are dynamic intracellular fluxes by the model simulation around the following network branching metabolites: D-glucose-6-phosphate, 6-phospho-D-gluconate, glyceraldehydes-3-phosphate, and pyruvate. The simulation of the fluxes around phosphoenolypyruvate show that phosphoenolpyruavate carboxylase and carboxykinase (PEPCK) activity and phosphotransferase system (PTS) are closely dynamically tied, indicating that glycolysis and TCA metabolisms can not be separated under the given transient conditions. Overall model adequacy is evaluated by standarddeviations of the model predictions and experimental data for each metabolite

Effect of к-carrageenan and NaCl on thermal properties of frozen surimi prepared from Adriatic pilchard

Samples of surimi were prepared under laboratory conditions from Adriatic pilchard (Sardina pilchardus). Water content in surimi was 81.5% before mixing with NaCl and κ-carrageenan, which were added in the range of mass fraction from 0 to 10%. Relative apparent specific enthalpy (H-), initial freezing point Ti, density p and thermal conductivity k of surimi in the temperature range from -25 °C to 10 °C were determined by differential thermal analysis (DTA), gravimetric method and line-heat source technique, respectively. For determination of relative apparent specific enthalpy (H-) the mathematical model of enthalpy based on orthogonal collocation approximation of DTA was applied. Redistributions of apparent enthalpy in the freezing range as functions of mass fractions of added substances were determined. Increase of mass fraction of added substances resulted in the increase of mass fraction of bound (unfreezable) water and lowered initial freezing point Ti, which has effects on the decrease of thermal conductivity k and increase of apparent specific enthalpy (H-) in the temperature range from -25 °C to Ti. This effect was more pronounced for samples where surimi was mixed with NaCl

In memory of Prof. Dr. Wolf-Dieter Deckwer

Editorial in memory of Prof. Dr. Wolf-Dieter Deckwe

Modelling of Activated Sludge Wastewater Treatment Process in Municipal Plant in Velika Gorica

Obrada otpadne vode s aktivnim muljem izrazito je složen fizikalni, kemijski i biološki proces. Nestalnost sastava i protoka otpadne vode te vremenski promjenjive reakcije mješovite kulture mikroorganizama uvjetuju njegovu nelinearnost i nestacionarnost. Optimalno vođenje procesa je kompleksan zadatak, ali pravilno izrađen matematički model može poslužiti kao osnova za rješavanje. U ovom radu izrađena su dva modela za opis procesa obrade otpadne vode na uređaju grada Velika Gorica, sa zadaćom predviđanja vrijednosti kemijske potrošnje kisika (KPK) na izlazu iz uređaja u zavisnosti od protoka i značajki ulazne neobrađene vode. Korišteni podaci dobiveni su svakodnevnim mjerenjem fizikalnih veličina koji ukazuju na kvalitetu ulazne i izlazne vode, na uvjete pri kojima uređaj radi te na kvalitetu aktivnog mulja. Za modeliranje procesa primijenjena je baza podataka od ukupno 223 skupa podataka sa po 26 parametara za kontinuirano razdoblje tijekom 2004. godine, koji su poslužili kao varijable za izradu modela. Iz ukupnog skupa veličina statističkom analizom odabrane su najuvjerljivije varijable za izradu modela. Izrađeni su viševarijantni linearni model (MLR) sa 16 varijabli te model lokalne linearne regresije (PLR) sa 17 varijabli. Srednja pogreška viševarijantnog linearnog modela iznosi γKPK=20 mg O2 L-1, a za model lokalne linearne regresije srednja pogreška iznosi γKPK = 16 mg O2 L-1.Activated sludge wastewater treatment is a highly complex physical, chemical and biological process, and variations in wastewater flow rate and its composition, combined with time-varying reactions in a mixed culture of microorganisms, make this process non-linear and unsteady. The efficiency of the process is established by measuring the quantities that indicate quality of the treated wastewater, but they can only be determined at the end of the process, which is when the water has already been processed and is at the outlet of the plant and released into the environment. If the water quality is not acceptable, it is already too late for its improvement, which indicates the need for a feed forward process control based on a mathematical model. Since there is no possibility of retracing the process steps back, all the mistakes in the control of the process could induce an ecological disaster of a smaller or bigger extent. Therefore, models that describe this process well may be used as a basis for monitoring and optimal control of the process development. This work analyzes the process of biological treatment of wastewater in the Velika Gorica plant. Two empirical models for the description of the process were established, multiple linear regression model (MLR) with 16 predictor variables and piecewise linear regression model (PLR) with 17 predictor variables. These models were developed with the aim to predict COD value of the effluent wastewater at the outlet, after treatment. The development of the models is based on the statistical analysis of experimental data, which are used to determine the relations among individual variables. In this work are applied linear models based on multiple linear regression (MLR) and partial least squares (PLR) methods. The used data were obtained by everyday measurements of the quantities that indicate the quality of the input and output water, working conditions of the plant and the quality of the activated sludge. The database contains 223 groups, each with 26 parameters, for the entire year of 2004. The variables were analyzed for determination of the most important factors. The analyses were done with significance level of p < 0.05, which is a commonly acceptable error level in the industrial process. The complex nature of the process and its sensitivity, depending on different factors, have been confirmed by the results of the analyses in this work. In all of the developed models the quantities that describe climatic influence, biological components and the quality of the "raw material" i.e. incoming wastewater, have been included as predictors. It is clear that the quality of the treated wastewaters, and thus the efficiency of the process are changing depending on a number of factors that influence the process differently. Even though more intricate models, like artificial intelligence, are used to describe such complex processes, it can be concluded that even such simple models like MLR and PLR can present the complexity and dynamics of this process with acceptable reliability. In this work for the developed models, the obtained average error of multiple linear regression model is γKPK = 16 mg L-1-O2and the average error of piecewise linear regression model is γKPK= 16 mg L-1- O2