1. Incorporating enriched empirical models into optimization algorithm to enhance biogas productionTina Kegl, Andreja Goršek, Darja Pečar, 2025, original scientific article Abstract: This paper introduces a novel approach to optimization of the anaerobic co-digestion (AcoD) process by developing enriched versions of first-order kinetic, modified Gompertz, and single-stage combined kinetic models. The key innovation of these enriched models lies in the introduction of new kinetic parameters that depend on both temperature and substrate composition, resulting in a set of new model parameters. These parameters are calibrated simultaneously across various process conditions, unlike existing models where kinetic constants are calibrated for only one operating regime. The enriched models are successfully calibrated and validated with experimental data from a batch AcoD of chicken manure with sawdust and fungal-pretreated Miscanthus; the relative index of agreement is higher than 0.99 for the produced biogas under all considered process conditions. By using the calibrated models to optimize the substrate composition and the AcoD process temperature profile, the results indicate that biogas production can increase by up to 50 %. Moreover, the proposed optimization allows for a favorable cost-benefit ratio; the estimated net energy gain can increase by up to 40 %. The proposed enriched models enable accurate prediction of biogas production at various process conditions and optimization of the AcoD process, representing a significant advancement over existing empirical models.
Keywords: biogas production, Pleurothus ostreatus, kinetic model parameters calibration, process optimization, gradient-based optimization, energy trade-offs
Published in DKUM: 29.05.2025; Views: 0; Downloads: 2
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2. Modeling and multi-objective optimization of forward osmosis processTina Kegl, Jasmina Korenak, Hermina Bukšek, Irena Petrinić, 2024, original scientific article Abstract: In order to ensure efficient wastewater treatment and seawater desalination, adequate modeling and optimization of the forward osmosis (FO) process has the potential to be very helpful. This paper deals with the FO model parameters calibration and FO process optimization by a gradient-based optimization method. For this purpose, an upgraded FO model, which involves temperature- and agent-dependent parameters, was developed. The FO model calibration was done using NaCl as agents in draw solution, while MgCl2 was used for model validation. The agreements between simulated and measured FO performance were satisfactory; relative index of agreement are higher than 0.99. By using the proposed FO model, the optimization of FO process conditions was performed with various definitions of the objective and constraint functions. In case of maximizing the water flux, minimizing reverse solute flux, and fulfilling the required constraints, the ratio of water flux and reverse solute flux increased up to 40 % for NaCl and up to 20 % for MgCl2; meanwhile the effective osmotic pressure difference was improved 2-times for NaCl and up to 3.8-times for MgCl2. The optimization process proved to be stable and efficient and can easily be adapted or upgraded for more complex dynamic FO modeling.
Keywords: forward osmosis, modeling, model and process parameters, calibration procedure, gadient-based optimization
Published in DKUM: 23.08.2024; Views: 67; Downloads: 36
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4. Segmenting risks in risk managementBorut Jereb, 2009, original scientific article Abstract: The paper describes a segmentation of risks to make each risk segment more manageable. The proposed approach is primarily intended to improve the confidentiality of risk simulations. The description of the approach is based on a logistics business process system which requires that its input is represented as a process graph. Each process is defined in terms of input and output; input comprises general input as well as risks; output comprises general output as well as impacts. The model takes into consideration internalas well as external input and output. Parameters can be used to define individual processes. Processes include functions that calculate new values of parameters and output on the bases of given input. Based on given tolerance levels for risks, impacts and process parameters, the model determines whether these levels are acceptable. The model assumes that parameters and functions are non-deterministic, i.e. parameters and functions may change in time. Although the approach is described on a very general level, each segment can be further subdivided into subsegments in order to include more characteristics of observed risks.
Keywords: risk, impact, segmentation, risk management, process parameters, logistics, model, simulation tools, non-deterministic
Published in DKUM: 05.06.2012; Views: 2069; Downloads: 55
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5. Analysis of growth models for batch kefir grain biomass production in RC1 reaction systemMarko Tramšek, Andreja Goršek, 2008, original scientific article Abstract: This work describes the statistical analysis of three mathematical models, modified for describing the kefir grain biomass growth curve. Experimental data of time-dependent kefir grain mass increase were used. The propagation was performed in RC1 batch reaction system under optimal bioprocess parameters (temperature, rotational frequency of stirrer, glucose mass concentration) using traditional cultivation in fresh, high-temperature, pasteurized whole fat cow's milk. We compared values of biological parameters obtained by applying the nonlinear regression of experimental data in logistic, Gompertz and Richards models. The most statistically appropriate model was determined using the seven statistical indicators. We established that the kefir grain biomass growth curve during batch propagation under optimal bioprocess conditions can be most successfully described using the Gompertz growth model.
Keywords: chemical processing, milk products, kefir grain growth, process parameters, design of experiments, modeling, mathematical models, Gompertz growth model, RC1
Published in DKUM: 31.05.2012; Views: 2990; Downloads: 137
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6. Using neural networks in the process of calibrating the microsimulation models in the analysis and design of roundabouts in urban areasIrena Ištoka Otković, 2011, dissertation Abstract: The thesis researches the application of neural networks in computer program calibration of traffic micro-simulation models. The calibration process is designed on the basis of the VISSIM micro-simulation model of local urban roundabouts.
From the five analyzed methods of computer program calibration, Methods I, II and V were selected for a more detailed research. The three chosen calibration methods varied the number of outgoing traffic indicators predicted by neural networks and a number of neural networks in the computer program calibration procedure. Within the calibration program, the task of neural networks was to predict the output of VISSIM simulations for selected functional traffic parameters - traveling time between the measurement points and queue parameters (maximum queue and number of stopping at the roundabout entrance). The Databases for neural network training consisted of 1379 combinations of input parameters whereas the number of output indicators of VISSIM simulations was varied. The neural networks (176 of them) were trained and compared for the calibration process according to training and generalization criteria. The best neural network for each calibration method was chosen by using the two-phase validation of neural networks.
The Method I is the calibration method based on calibration of a traffic indicator -traveling time and it enables validation related to the second observed indicator – queue parameters. Methods II and V connect the previously described calibration and validation procedures in one calibration process which calibrates input parameters according to two traffic indicators.
Validation of the analyzed calibration methods was performed on three new sets of measured data - two sets at the same roundabout and one set on another location. The best results in validation of computer program calibration were achieved by the Method I which is the recommended method for computer program calibration.
The modeling results of selected traffic parameters obtained by calibrated VISSIM traffic model were compared with: values obtained by measurements in the field, the existing analysis methods of operational roundabouts characteristics (Lausanne method, Kimber-Hollis, HCM) and modeling by the uncalibrated VISSIM model. The calibrated model shows good correspondence with measured values in real traffic conditions. The efficiency of the calibration process was confirmed by comparing the measured and modeled values of delays, of an independent traffic indicator that was not used in the process of calibration and validation of traffic micro-simulation models.
There is also an example of using the calibrated model in the impact analysis of pedestrian flows on conflicting input and output flows of vehicles in the roundabout. Different traffic scenarios were analyzed in the real and anticipated traffic conditions.
Keywords: traffic models, traffic micro-simulation, calibration of the VISSIM model, computer program calibration method, neural networks in the calibration process, micro-simulation of roundabouts, traffic modeling parameters, driving time, queue parameters, delay
Published in DKUM: 02.06.2011; Views: 5471; Downloads: 395
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