The irregularity profiles of steel samples after vapour blasting were measured. A correlation analysis of profile parameters was then carried out. As the result, the following parameters were selected: Pq, Pt, PDq, Pp/Pt and Pku. Surface profiles after vapour blasting were modeled. The modeled surfaces were correctly matched to measured surfaces in 78% of all analyzed cases. The vapour blasting experiment was then carried out using an orthogonal selective research plan. The distance between the nozzle and sample d and the pressure of feed system p were input parameters; selected surface texture coefficients were output parameters. As the result of the experiment, regression equations connecting vapour blasting process parameters p and d with selected profile parameters were obtained. Finally, 2D profiles of steel samples were forecasted for various values of vapour blasting parameters. Proper matching accuracy of modeled to measured profiles was assured in 75% of analyzed cases.
Potato white mold caused by Sclerotinia sclerotiorum is an important plant disease occurring in many potato-producing areas throughout the world. In this study, a specific diagnostic method was used to detect and quantify S. sclerotiorum ascospores, and its forecasting ability was assessed in potato fields during flowering periods of 2011 to 2014 in Bahar County, Hamedan Province. Using GenEMBL database, a primer pair, HZSCREV and HZSCFOR, was designed and optimized for the pathogen. After testing the sensitivity of primers, DNA was extracted from samples of outdoor Burkard traps from potato fields. A linear association was observed between pathogen DNA and the number of ascospores using the quantitative PCR (qPCR) technique in the presence of SYBR dye. The qPCR could successfully detect DNA amounts representing two S. sclerotiorum ascospores and was not sensitive to a variety of tested fungi such as Botrytis cinerea, Alternaria brassicae, Fusarium solani. In contrast to the amount of rainfall, a direct relationship was found between ascospore numbers and the incidence of potato white mold from 2011 to 2014.
In this article, we review the research state of the bullwhip effect in supply chains with stochastic lead times. We analyze problems arising in a supply chain when lead times are not deterministic. Using real data from a supply chain, we confirm that lead times are stochastic and can be modeled by a sequence of independent identically distributed random variables. This underlines the need to further study supply chains with stochastic lead times and model the behavior of such chains.
This study involves the implementation of an economic order quantity (EOQ) model which is an inventory control method in a ceramic factory. Two different methods were applied for the calculation of EOQs. The first method is to determine EOQ values using a response surface method-based approach (RSM). The second method uses conventional EOQ calculations. To produce a ceramic product, 281 different and additive materials may be used. First, Pareto (ABC) analysis was performed to determine which of the materials have higher priority. Because of this analysis, the value of 21 items among 281 different materials and additives were compared to the ratio of the total product. The ratio was found to be 70.4% so calculations were made for 21 items. Usage value for every single item for the years 2011, 2012, 2013 and 2014, respectively, were obtained from the company records. Eight different demand forecasting methods were applied to find the amount of the demand in EOQ. As a result of forecasting, the EOQ of the items were calculated by establishing a model. Also, EOQ and RSM calculations for the items were made and both calculation results were compared to each other. Considering the obtained results, it is understood that RSM can be used in EOQ calculations rather than the conventional EOQ model. Also, there are big differences between the EOQ values which were implemented by the company and the values calculated. Because of this work, the RSM-based EOQ approach can be used to decide on the EOQ calculations as a way of improving the system performance.
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.