The object of the present study is to investigate the influence of damping uncertainty and statistical correlation on the dynamic response of structures with random damping parameters in the neighbourhood of a resonant frequency. A Non-Linear Statistical model (NLSM) is successfully demonstrated to predict the probabilistic response of an industrial building structure with correlated random damping. A practical computational technique to generate first and second-order sensitivity derivatives is presented and the validity of the predicted statistical moments is checked by traditional Monte Carlo simulation. Simulation results show the effectiveness of the NLSM to estimate uncertainty propagation in structural dynamics. In addition, it is demonstrated that the uncertainty in damping indeed influences the system response with the effects being more pronounced for lightly damped structures, higher variability and higher statistical correlation of damping parameters.

The sustainable management of energy production and consumption is one of the main challenges of the 21st century. This results from the threats to the natural environment, including the negative impact of the energy sector on the climate, the limited resources of fossil fuels, as well as the unstability of renewable energy sources – despite the development of technologies for obtaining energy from the: sun, wind, water, etc. In this situation, the efficiency of energy management, both on the micro (dispersed energy) and macro (power system) scale, may be improved by innovative technological solutions enabling energy storage. Their effective implementation enables energy storage during periods of overproduction and its use in the case of energy shortages. These challenges cannot be overestimated. Modern science needs to solve various technological issues in the field of storage, organizational problems of enterprises producing electricity and heat, or issues related to the functioning of energy markets. The article presents the specificity of the operation of a combined heat and power plant with a heat accumulator in the electricity market while taking the parameters affected by uncertainty into account. It was pointed out that the analysis of the risk associated with energy prices and weather conditions is an important element of the decision-making process and management of a heat and power plant equipped with a cold water heat accumulator. The complexity of the issues and the number of variables to be analyzed at a given time are the reason for the use of advanced forecasting methods. The stochastic modeling methods are considered as interesting tools that allow forecasting the operation of an installation with a heat accumulator while taking the influence of numerous variables into account. The analysis has shown that the combined use of Monte Carlo simulations and forecasting using the geometric Brownian motion enables the quantification of the risk of the CHP plant’s operation and the impact of using the energy store on solving uncertainties. The applied methodology can be used at the design stage of systems with energy storage and enables carrying out the risk analysis in the already existing systems; this will allow their efficiency to be improved. The introduction of additional parameters of the planned investments to the analysis will allow the maximum use of energy storage systems in both industrial and dispersed power generation.

With the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of charging behaviour such as the starting time and charging duration are analysed. The results show that charging distribution is in line with a logarithmic normal distribution. An EV charging behaviour model is established, and error calibration is carried out. The result shows that the error is within its permitted scope. The daily EV charge load is obtained by using the Latin hypercube Monte Carlo statistical method. Genetic particle swarm optimization (PSO) is proposed to optimize the proportion of AC 1, AC 2 and DC charging equipment, and the optimal solution can not only meet the needs of users but also reduce equipment investment and the EV peak valley difference, so the effectiveness of the method is verified.

Basic gesture sensors can play a significant role as input units in mobile smart devices. However, they have to handle a wide variety of gestures while preserving the advantages of basic sensors. In this paper a user-determined approach to the design of a sparse optical gesture sensor is proposed. The statistical research on a study group of individuals includes the measurement of user-related parameters like the speed of a performed swipe (dynamic gesture) and the morphology of fingers. The obtained results, as well as other a priori requirements for an optical gesture sensor were further used in the design process. Several properties were examined using simulations or experimental verification. It was shown that the designed optical gesture sensor provides accurate localization of fingers, and recognizes a set of static and dynamic hand gestures using a relatively low level of power consumption.

The main aim of this research is to compare the results of the study of demand’s plan and

standardized time based on three heuristic scheduling methods such as Campbell Dudek

Smith (CDS), Palmer, and Dannenbring. This paper minimizes the makespan under certain

and uncertain demand for domestic boxes at the leading glass company industry in Indonesia.

The investigation is run in a department called Preparation Box (later simply called PRP)

which experiences tardiness while meeting the requirement of domestic demand. The effect

of tardiness leads to unfulfilled domestic demand and hampers the production department

delivers goods to the customer on time. PRP needs to consider demand planning for the

next period under the certain and uncertain demand plot using the forecasting and Monte

Carlo simulation technique. This research also utilizes a work sampling method to calculate

the standardized time, which is calculated by considering the performance rating and

allowance factor. This paper contributes to showing a comparison between three heuristic

scheduling methods performances regarding a real-life problem. This paper concludes that

the Dannenbring method is suitable for large domestic boxes under certain demand while

Palmer and Dannenbring methods are suitable for large domestic boxes under uncertain

demand. The CDS method is suitable to prepare small domestic boxes for both certain and

uncertain demand.

According to the European Environment Agency (EEA 2018), air quality in Poland is one of the worst in Europe. There are several sources of air pollution, but the condition of the air in Poland is primarily the result of the so-called low-stack emissions from the household sector. The main reason for the emission of pollutants is the combustion of low-quality fuels (mainly low-quality coal) and waste, and the use of obsolete heating boilers with low efficiency and without appropriate filters. The aim of the study was to evaluate the impact of measures aimed at reducing low-stack emissions from the household sector (boiler replacement, change of fuel type, and thermal insulation of buildings), resulting from environmental regulations, on the improvement of energy efficiency and the emission of pollutants from the household sector in Poland. Stochastic energy and mass balance models for a hypothetical household, which were used to assess the impact of remedial actions on the energy efficiency and emission of pollutants, have been developed. The annual energy consumption and emissions of pollutants were estimated for hypothetical households before and after the implementation of a given remedial action. The calculations, using the Monte Carlo simulation, were carried out for several thousand hypothetical households, for which the values of the technical parameters (type of residential building, residential building area, unitary energy demand for heating, type of heat source) were randomly drawn from probability distributions developed on the basis of the analysis of the domestic structure of households. The model takes the coefficients of correlation between the explanatory variables in the model into account. The obtained results were multiplied so that the number of hypothetical households was equal to 14.1 million, i.e. the real number of households in Poland. The obtained results allowed for identifying the potential for reducing the emission of pollutants such as carbon dioxide, carbon monoxide, dust, and nitrogen oxides, and improving the energy efficiency as a result of the proposed and implemented measures, aimed at reducing low-stack emission, resulting from the policy.

The potential for emissions of gaseous pollutants is 94% for CO, 49% for NOx, 90% for dust, and 87% for SO2. The potential for improving the energy efficiency in households is around 42%.

The purposes of this study were to investigate the impact of proportions of cast iron scrap, steel scrap, carbon and ferro silicon on hardness and the quality of cast iron and to obtain an appropriate proportion of the four components in iron casting process using a mixture experimental design, analysis of variance and response surface methodology coupled with desirability function. Monte Carlo simulation was used to demonstrate the impacts of different proportions of the four components by varying the proportions of components within ±5% of the four components. Microstructures of the cast iron sample obtained from a company and the cast iron samples casted with the appropriate proportions of the four components were examined to see the differences of size and spacing of pearlite particle. The results showed that linear mixture components were statistically significant implying a high proportion of total variability for hardness of the cast iron samples explained by the casting mixtures of raw materials. The graphite of the sample casted from the appropriate proportion has shorter length and more uniform distribution than that from the company. When varying percentages of the four components within ±5% of the appropriate proportion, simulated hardness values were in the range of 237 to 256 HB.

This paper aims to enhance the productivity of a chilled beef production line by comparing two techniques; standard time calculation and simulation. The best improvement method was obtained using the work-study principle, a network diagram, and bottleneck identification. Two methods for improvement are proposed based on the ECRS, the Theory of Constraint (TOC), and line balancing concepts. A simulation model is developed to mimic the actual production line. The simulation results are verified, validated, and compared. Some workstations were combined, and the allocation of the workers was arranged. The present production line efficiency was 46.21%, which increased to 67.09% and 79.71% from the suggested methods. It showed that using the standard time calculation gives a different result from the simulation. In summary, the simulation model along with the application of TOC and ECRS, provides accurate information and improves overall productivity.

The safety of the masonry structure is determined by the value of the partial factor used, which is influenced by many factors. The variability of these factors determines obtaining significant differences in the load levels of various masonry structures. Hence, the analysis of masonry structures should be carried out taking into account a sufficient range of variability of factors affecting its safety. The article presents a multi-stage safety analysis of an exemplary brick masonry column. For the construction, the relationship between partial factors used for interactions in different configurations and factors for the masonry compressive strength was examined. The analyses consisted in determining the reliability index beta with the Monte Carlo method. The article presents the results of experimental tests carried out on a real construction, as well as the results of FEM numerical simulations.

The paper considers the modeling and estimation of the stochastic frontier model where the error components are assumed to be correlated and the inefficiency error is assumed to be autocorrelated. The multivariate Farlie-Gumble-Morgenstern (FGM) and normal copula are used to capture both the contemporaneous and the temporal dependence between, and among, the noise and the inefficiency components. The intractable multiple integrals that appear in the likelihood function of the model are evaluated using the Halton sequence based Monte Carlo (MC) simulation technique. The consistency and the asymptotic efficiency of the resulting simulated maximum likelihood (SML) estimators of the present model parameters are established. Finally, the application of model using the SML method to the real life US airline data shows significant noise-inefficiency dependence and temporal dependence of inefficiency.

In this work, a fast 32-bit one-million-channel time interval spectrometer is proposed based on field programmable gate arrays (FPGAs). The time resolution is adjustable down to 3.33 ns (= T, the digitization/discretization period) based on a prototype system hardware. The system is capable to collect billions of time interval data arranged in one million timing channels. This huge number of channels makes it an ideal measuring tool for very short to very long time intervals of nuclear particle detection systems. The data are stored and updated in a built-in SRAM memory during the measuring process, and then transferred to the computer. Two time-to-digital converters (TDCs) working in parallel are implemented in the design to immune the system against loss of the first short time interval events (namely below 10 ns considering the tests performed on the prototype hardware platform of the system). Additionally, the theory of multiple count loss effect is investigated analytically. Using the Monte Carlo method, losses of counts up to 100 million events per second (Meps) are calculated and the effective system dead time is estimated by curve fitting of a non-extendable dead time model to the results (τNE = 2.26 ns). An important dead time effect on a measured random process is the distortion on the time spectrum; using the Monte Carlo method this effect is also studied. The uncertainty of the system is analysed experimentally. The standard deviation of the system is estimated as ± 36.6 × T (T = 3.33 ns) for a one-second time interval test signal (300 million T in the time interval).

The paper deals with application of the Gumbel model to evaluation of the environmental loads. According to recommendations of Eurocodes, the conventional method of determining return period and characteristic values of loads utilizes the theory of extremes and implicitly assumes that the cumulative distribution function of the annual or other basic period extremes is the Gumbel distribution. However, the extreme value theory shows that the distribution of extremes asymptotically approaches the Gumbel distribution when the number of independent observations in each observation period from which the maximum is abstracted increases to infinity. Results of calculations based on simulation show that in practice the rate of convergence is very slow and significantly depends on the type of parent results distribution, values of coefficient of variation, and number of observation periods. In this connection, a straightforward purely empirical method based on fitting a curve to the observed extremes is suggested.

In the existent world of continuous production systems, strong attention has been waged

to anonymous risk that probably generates significant apprehension. The forecast for net

present value is extremely important for any production plant. The objective of this paper

is to implement Monte Carlo simulation technique for perceiving the impact of risk and uncertainty

in prediction and forecasting company’s profitability. The production unit under

study is interested to make the initial investment by installing an additional spray dryer

plant. The expressive values acquied from the Monte Carlo technique established a range of

certain results. The expected net present value of the cash flow is $14,605, hence the frequency

chart outcomes confirmed that there is the highest level of certainty that the company

will achieve its target. To forecast the net present value for the next period, the results

confirmed that there are 50.73% chances of achieving the outcomes. Considering the minimum

and maximum values at 80% certainty level, it was observed that 80% chances exist

that expected outcomes will be between $5,830 and $22,587. The model’s sensitivity results

validated that cash inflows had a greater sensitivity level of 21.1% and the cash inflows for

the next year as 19.7%. Cumulative frequency distribution confirmed that the probability

to achieve a maximum value of $23,520 is 90 % and for the value of $6,244 it is about 10 %.

These validations suggested that controlling the expenditures, the company’s outflows can

also be controlled definitely.

The mean-reversion model is introduced into the study of mineral product price prediction. The gold price data from January 2018 to December 2021 are selected, and a mean-reverting stochastic process simulation of the gold price was carried out using Monte Carlo simulation (MCS) method. By comparing the statistical results and trend curves of the mean-reversion (MR) model, geometric Brownian motion (GBM) model, time series model and actual price, it is proved that the mean-reversion process is valid in describing the price fluctuation of mineral product. At the same time, by comparing with the traditional prediction methods, the mean-reversion model can quantitatively assess the uncertainty of the predicted price through a set of equal probability stochastic simulation results, so as to provide data support and decision-making basis for the risk analysis of future economy.

The aim of the study was to model the operation of a wastewater treatment plant using the Monte Carlo method and selected probability distributions of random variables. Pollutant indices in treated wastewater were analysed, such as: biological oxygen demand ( BOD ₅), chemical oxygen demand ( COD _Cr), total suspended solids ( TSS), total nitrogen (N _tot), total phosphorus (P _tot). The preliminary analysis of pollution indicators series included the: calculation of descriptive statistics and assessment of biological degradability of wastewater. The consistency of the theoretical distributions with the empirical ones was assessed using Anderson–Darling statistics. The best-fitting statistical distributions were selected using the percent bias criterion. Based on the calculations performed, it was found that the analysed indicators of pollution in treated wastewater were characterised by an average variability of composition for BOD ₅, COD _Cr and TSS, and a high variability of composition for N _tot and P _tot. The best fitted distribution was log-normal for BOD ₅, TSS, N _tot and P _tot and general extreme values for COD _Cr. The simulation carried out using the Monte-Carlo method confirmed that there may be problems associated with the reduction of nutrients (N _tot and P _tot) the analysed wastewater treatment plant. Results of values obtained of the risk values of negative control of wastewater treatment plant operation for biogenic compounds, different from 1, indicate that the number of exceedances at the outflow may be higher than the acceptable one.

One of difficulties of working with pulse mode detectors is dead time and its distorting effect on measuring with the random process. Three different models for description of dead time effect are given, these are paralizable, non-paralizable, and hybrid models. The first two models describe the behaviour of the detector with one degree of freedom. But the third one which is a combination of the other two models, with two degrees of freedom, proposes a more realistic description of the detector behaviour. Each model has its specific observation probability. In this research, these models are simulated using the Monte Carlo method and their individual observation probabilities are determined and compared with each other. The Monte Carlo simulation, is first validated by analytical formulas of the models and then is utilized for calculation of the observation probability. Using the results, the probability for observing pulses with different time intervals in the output of the detector is determined. Therefore, it is possible by comparing the observation probability of these models with the experimental result to determine the proper model and optimized values of its parameters. The results presented in this paper can be applied to other pulse mode detection and measuring systems of physical stochastic processes.

The purpose of this paper is to investigate the effects of natural uncertainties and effective parameters on the stability of plate-type rock walls. For this, the effective factors and geo-mechanical properties in the study area were obtained using field experiments. Stability analysis of rock walls was investigated for 40 scenarios in dry and saturated states. These parameters were then evaluated using Easyfit software and Markov chain analysis and Monte Carlo simulation by Rock Plane software. Comparison of the results of numerical and uncertainty methods shows that the rock walls with 60-80 degree slope are stable; and In saturated state they require stability due to the reduction of shear strength. Fixation of the rock walls was also investigated, indicating an optimum angle of 30° for the installation of the rock screw. The results show that the Monte Carlo simulation provides a simpler interpretation and the uncertainty methods are more accurate and reliable than the numerical methods.

Quality profiling seeks to know the quality characteristics of products and processes to improve customer satisfaction and business competitiveness. It is required to develop new techniques and tools that upgrade and complement the traditional analysis of process variables. This article proposes a new methodology to model quality control of the process and product quality characteristics by applying optimization and simulation tools. The application in the production process of carbonated beverages allowed us to identify the most influential variables on the gas content and the degrees Brix of beverage.

Value stream mapping (VSM) is a well-known lean analytical tool in identifying wastes, value, value stream, and flow of materials and information. However, process variability is a waste that traditional VSM cannot define or measure since it is considered as a static tool. For that, a new model named Variable Value Stream Mapping (V-VSM) was developed in this study to integrate VSM with risk management (RM) using Monte Carlo simulation. This model is capable of generating performance statistics to define, analyze, and show the impact of variability within VSM. The platform of this integration is under Deming’s Plan-Do-Check-Act (PDCA) cycle to systematically implement and conduct V-VSM model. The model has been developed and designed through literature investigation and reports that lead in defining the main four concepts named as; Continuous Improvement, Data Variability, Decision-Making, and Data Estimation. These concepts can be considered as connecting points between VSM, RM and PDCA.

This paper concerns load testing of typical bridge structures performed prior to operation. In-situ tests of a twospan post-tensioned bridge loaded with three vehicles of 38-ton mass each formed the input of this study. On the basis of the results of these measurements an advanced FEM model of the structure was developed for which the sensitivity analysis was performed for chosen uncertainty sources. Three uncorrelated random variables representing material uncertainties, imperfections of positioning and total mass of loading vehicles were indicated. Afterwards, two alternative FE models were created based on a fully parametrised geometry of the bridge, differing by a chosen global parameter – the skew angle of the structure. All three solid models were subjected to probabilistic analyses with the use of second-order Response Surface Method in order to define the features of structural response of the models. It was observed that both the ranges of expected deflections and their corresponding mean values decreased with an increase of the skewness of the bridge models. Meanwhile, the coefficient of variation and relative difference between the mean value and boundary quantiles of the ranges remain insensitive to the changes in the skew angle. Owing to this, a procedure was formulated to simplify the process of load testing design of typical bridges differing by a chosen global parameter. The procedure allows - if certain conditions are fulfilled - to perform probabilistic calculations only once and use the indicated probabilistic parameters in the design of other bridges for which calculations can be performed deterministically.

The paper presents the assessment of reliability depending on the reinforcement cover thickness for elements subject to bending. Based on the experimental tests of 12 reinforced concrete beams subjected to four-point bending the numerical model was validated. In the next steps this numerical model was used for the Monte Carlo simulation. During the analyses the failure probability and the reliability index were determined by two methods – using probabilistic method –FORMand fully probabilistic method Monte Carlo with the use of variance reduction techniques by Latin hypercube sampling (LHS). The random character of input data – compressive strength of concrete, yield strength of steel and effective depth of reinforcement were assumed in the analysis. Non-parametric Spearman rank correlation method was used to estimate the statistical relationship between random variables. Analyses have shown a significant influence of the random character of effective depth on reliability index and the failure probability of bending elements.