Academic literature on the topic 'Petroleum products Prices Computer simulation'

Background: In the domestic market, the consumption of fuel oil increases during the winter period, leading to higher prices. At the same time, the cost of inputs and the time for the discharge of viscous oil products are greatly increased. The duration of the discharge process is related to the physico-chemical properties of the fuel oil. Its viscosity depends on the temperature of the product itself and the temperature of the environment, which in our country averages 5.5 C per year. Aim: Reduction in the length and cost of transport of viscous petroleum products. Methods: The article proposes a new method for the carriage of viscous petroleum products by rail, ensuring that their fluidity is preserved without the use of thermal insulation of the boiler of the tank-wagon and the means for carrying the heating. Simulation models of the processes of pouring out viscous petroleum products for a traditional and new method of pouring in the circulation method of discharge of viscous petroleum products, which make it possible to estimate the quantity of resources consumed, are constructed. Results: The work shows the peculiarities of the existing process of discharging viscous petroleum products. Simulation and functional-cost analysis of the discharge process were carried out under the circulatory method for heating viscous petroleum products. The results were compared. Conclusion: In the new pouring method, the discharge process is similar to the summer period.

Historically, petroleum fuels have been the dominant fuel used for land transport. However, the growing need for sustainable national economics has urged us to incorporate more economical and ecological alternative vehicle fuels. The advantages and disadvantages of them complicate the decision-making process and compel us to develop adequate mathematical methods. Alternative fuel (compressed natural gas, liquefied petroleum gas, and ethanol fuel mixtures), the standard prices and their ratios were investigated. A mathematical model to determine a critical ratio between alternative and conventional fuel prices had already been developed. The results of this were investigated. The results showed that the critical ratio is not a linear function on annual conventional fuel consumption costs. According to our simulation gaseous fuels were economically more attractive. Whereas, the use of bioethanol blends had more risk.

During the last decade, the rising trend in energy prices and its potential effect on food prices have become a controversial issue between policy-makers and economists. Therefore, research addressing the relationship between food and macroeconomic variables, such as energy prices, will be useful in providing information for the design of appropriate economic policies. This study uses data from Iran to examine the impacts (short- and long-term) of exchange rate and energy prices on food prices. Iran is a good case study as in recent years its consumers have faced a rapid increase in both fuel and food prices. The variables employed in this study are the prices of ten food products, exchange rate (the value of Iranian rial per US dollar), and petroleum prices. All data in this study are from the Statistical Centre of Iran (SCI). We employ the panel unit root test, Pedroni co-integration tests, Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimation techniques, applied to a panel of monthly prices for ten food products for the period of March 1995 to February 2018. Results show that in both the short- and long-run, food prices would increase in response to an increase in energy prices. Findings also suggest that the appreciation of the United States Dollar (USD) in terms of the Iranian rial exerts a positive and significant impact on food prices in the long run.

In determining the magnitude of oil shocks to the economies of Southern Africa, it is essential that we examine the various components of vulnerability, as well as the crude oil price movements and the relationship between energy and development. Because energy consumers and producers are constrained by their energy consuming appliances which are fixed n the short-run, thus making it difficult to shift to less oil intensive means of production in response to higher oil prices, oil price shocks increase the total import bill for a country largely because of the huge increase in the cost of oil and petroleum products. Low-income countries and poorer households tend to suffer the largest impact from oil price rise

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

In this paper are presented the results obtained from a simulation of a pollution situation with crude oil products and the efficiency of the use of hydrocarbon recovery systems. In the event of major pollution accidents, in which the oil moves and touches the sandy drum, the only way of disposing is to load the polluted sand and to bring another in place, very costly and difficult operation. So, recovering a large amount of oil offshore is highly recommended. The phenomenon that governess the dynamics of pollution has been simulated using CFD (Computer Fluid Dynamics) and involves as follows: - In the first phase, the oil stain extends as the surface and thinning as thickness (thickness); -In the second phase, the mixing of hydrocarbon with water occurs, and the mixture becomes more and more difficult and no longer floats but moves immerse, but not less dangerous for the marine flora/fauna. This second phenomenon is favored by the state of the sea (the excitement of the water due to the waves). At the same time part of the hydrocarbon is volatile in the air, proportional with the temperature of water and air. Results of simulation are: a) the evolution of the quantity of petroleum product discharged during the simulation; b) the evolution of the quantity of evaporated petroleum product; c) the variation of the maximum thickness of the oil product film; d the variation of polluted surface; e) the variation of the quantity of petroleum product recovered; f) minimizing the risks of pollution by simulating scenarios with PISCES II software. The simulations on the movement and evolution of the oil film on the water surface are necessary for assessing the environmental impact, assessing the response time of the authorities in emergencies, assessing the situation, establishing The most sustainable strategies for limiting the scattering and isolation of petroleum products and the use of the most appropriate methods of response for the removal of petroleum products from the surface of the sea water or the shoreline.

In this paper, a multi-product newsvendor problem is formulated as a random nonlinear integrated optimization model by taking into consideration the selling price, the producing and outsourcing quantities, and the nonlinear budget constraint. Different from the existing models, the demands of products depend on the prices, as well as being time-varying due to random market fluctuation. In addition, outsourcing strategy is adopted to deal with possible shortage caused by the limited capacity. Consequently, the constructed model is involved with joint optimization of the producing and outsourcing quantities, and the selling prices of all the products. For this model with continuous random demands, we first transform it into a nonlinear programming problem by expectation method. Then, an efficient algorithm, called the feasible-direction-based spectral conjugate gradient algorithm, is developed to find a robust solution of the model. By case study and sensitivity analysis, some interesting conclusions are drawn as follows: (a) Budget is a critical constraint for optimizing the decision-making of the retailer, and there exist different threshold values of the budget for the substitute and complementarity scenarios. (b) The price sensitivity matrix seriously affects the maximal expected profit mainly through affecting the optimal outsourcing quantity.

We consider a firm selling two similar products with different prices. When the lower-priced product is out of stock, the firm supplies the customer a higher-priced product with the lower-priced product price. The policy is called free upgrade. In this paper, we study the profitability of such policy. We formulate the problem as a two-product newsvendor problem, and prove that the firm achieves more profit with the free upgrade policy. We propose a simulation algorithm to find the solution and demonstrate our findings with some numerical examples.

Portfolio optimization is one of the most complex problems in the financial field, and technical analysis is a popular tool to find an optimal solution that maximizes the yields. This paper establishes a portfolio optimization model consisting of a weighted unidirectional dual-layer LSTM model and an SMA-slope strategy. The weighted unidirectional dual-layer LSTM model is developed to predict the daily prices of gold/Bitcoin, which addresses the traditional problem of prediction lag. Based on the predicted prices and comparison of two representative investment strategies, simple moving average (SMA) and Bollinger bands (BB), this paper adopts a new investment strategy, SMA-slope strategy, which introduces the concept of k-slope to measure the daily ups and downs of gold/Bitcoin. As two typical financial products, gold and Bitcoin are opposite in terms of their characteristics, which may represent many existing financial products in investors’ portfolios. With a principle of $1000, this paper conducts a five-year simulation of gold and Bitcoin trading from 11 September 2016 to 10 September 2021. To compensate for the SMA and BB that may miss buying and selling points, 4 different parameters’ values in the k-slope are obtained through particle swarm optimization simulation. Also, the simulation results imply that the proposed portfolio optimization model contributes to helping investors make investment decisions with high profitability.

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.

CAPES
O segmento downstream da cadeia de suprimentos da indústria brasileira de petróleo é composta por bases de produção (e.g. refinarias), armazenamento (e.g. terminais) e consumo (e.g. mercados consumidores) e modais de transportes (e.g. oleodutos, navios, caminhões e trens). O planejamento da transferência de derivados de petróleo nesta rede multimodal é um problema complexo e atualmente é realizado para um horizonte de três meses com base na experiência de profissionais e sem auxílio de um sistema computacional de apoio à decisão. Basicamente, o problema pode ser visto como uma negociação para alocação de recursos disponíveis (tais como derivados de petróleo, tanques e modais de transporte) pelas diferentes bases envolvidas que necessitam enviar ou receber derivados de petróleo. Na literatura, alguns problemas semelhantes, porém mais voltados para o planejamento de redes formadas por um único tipo de modal de transporte, têm sido tratados por diferentes abordagens, com predominância da programação matemática. Estes trabalhos ilustram a difícil tarefa de modelar grandes problemas por meio desta abordagem. Geralmente, tais trabalhos consideram apenas um curto horizonte de planejamento ou apenas uma parte do problema original, tal como uma parte da rede petrolífera brasileira, gerando limitações importantes para os modelos desenvolvidos. Devido às características do problema em estudo, o qual envolve toda a rede de transporte e apresenta perfil de negociação entre as diferentes entidades envolvidas, surge o interesse da utilização do paradigma de sistemas multiagente. O paradigma de agentes tem sido aplicado a problemas de diferentes contextos, particularmente em problemas de gerenciamento de cadeias de suprimentos devido à sua correspondência natural com a realidade e, em geral, em problemas que envolvem a competição por recursos por meio de mecanismos de negociação com base em leilões. Este trabalho apresenta um novo protocolo de negociação baseado em leilões e aplicação deste protocolo em forma de um modelo multiagente na resolução do problema de planejamento em questão. Os agentes que formam a solução representam principalmente os locais de produção, armazenamento, consumo e os modais de transporte na rede petrolífera brasileira. O objetivo destes agentes é manter um nível de estoque diário factível de cada produto em cada local por meio de transferências de produtos pela rede petrolífera brasileira com preferível redução do custo de transporte. Por fim, este trabalho apresenta a satisfação destes objetivos por meio de experimentos em cenários fictícios e reais da rede brasileira de petróleo.
The Brazilian oil supply chain is composed by oil refineries, consumer markets, terminals for intermediary storage and several transportation modals, such as pipelines, ships, trucks and trains. The transportation planning of oil products in this multimodal network is a complex problem that is currently performed manually based on expertise, for a period of three months, due to the lack of a software system to cover the problem complexity. Such problem involves the negotiation of available resources such as oil products, tanks and transportation modals between different sources and consumption points. Similar problems, but more directed to the planning of single modes of transportation, have been treated by different approaches, mainly mathematical programming. Such works illustrate the difficult task of modeling large problems with this mechanism. Generally, they consider a short horizon planning or only part of the original problem, such as a part of the network, rendering important limitations to the models developed. Due to the characteristics of the problem in study where the full network needs to be considered and there exists negotiation amongst the different entities involved, the usage of multi-agent models seems to be worth to explore. Such models have been applied in different contexts such as to supply chain problems due its natural correspondence with the reality. Furthermore, in problems involving competition for resources, multi-agents negotiation mechanisms based on auctions are commonly applied. Thus, this thesis presents one auction-based solution formed by the cooperation among agents for them to achieve their goals. The agents involved in the auctions represent mainly the production, storage and consumption locations. Their goal is to maintain a daily suitable inventory level for each product by means of transportation through the multimodal network at a low transport cost. Finally, this paper presents the satisfaction of these objectives through experiments on real and fictional scenarios of Brazilian oil network.

Plug-in hybrid electric vehicle (PHEV) technology has the potential to help address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption from the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an integrated optimization model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation of vehicles to drivers for minimum life cycle cost, GHG emissions, and petroleum consumption. We find that, while PHEVs with large battery capacity minimize petroleum consumption, a mix of PHEVs sized for 25–40 miles of electric travel produces the greatest reduction in lifecycle GHG emissions. At today’s average US energy prices, battery pack cost must fall below $460/kWh (below $300/kWh for a 10% discount rate) for PHEVs to be cost competitive with ordinary hybrid electric vehicles (HEVs). Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs even at $100/tonne. We find that the maximum battery swing should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption. Increased swing enables greater all-electric range (AER) to be achieved with smaller battery packs, improving cost competitiveness of PHEVs. Hence, existing policies that subsidize battery cost for PHEVs would likely be better tied to AER, rather than total battery capacity.

Abstract Fluid Catalytic Cracking (FCC) technology is the most important process used by the refinery industry to convert crude oil to valuable lighter products such as gasoline. Process development is generally very time consuming especially when a small pilot unit is being scaled-up to a large commercial unit because of the lack of information to aide in the design of scaled-up units. Such information can now be obtained by analysis based on the pilot scale measurements and computer simulation that includes controlling physics of the FCC system. A Computational fluid dynamic (CFD) code, ICRKFLO, has been developed at Argonne National Laboratory (ANL) and has been successfully applied to the simulation of catalytic petroleum cracking risers. It employs hybrid hydrodynamic-chemical kinetic coupling techniques, enabling the analysis of an FCC unit with complex chemical reaction sets containing tens or hundreds of subspecies. The code has been continuously validated based on pilot-scale experimental data. It is now being used to investigate the effects of scaled-up FCC units. Among FCC operating conditions, the feed injection conditions are found to have a strong impact on the product yields of scaled-up FCC units. The feed injection conditions appear to affect flow and heat transfer patterns and the interaction of hydrodynamics and cracking kinetics causes the product yields to change accordingly.

The use of drag reduction agents (DRA) can be a decisive factor in determining the technical and economic feasibility of new pipelines projects, meeting the demands not foreseen and seasonality accommodation without large investments in infrastructure. Knowing the friction reduction mechanism and its impact on the operating procedure of existing products is essential in order to have the guarantee of the benefit for your application. Most of the works published report field experiences obtained from its application, seeking to determine the influence that internal and external factors have on the polymer. Knowing these effects is essential for better application performance. However, few authors have sought to identify the best way to operate an existing pipeline with DRA, with either an increase in capacity or an energy reduction. Operationally, the use of drag reducing agents may decrease the currently used arrangement of pumps, or even the complete shutdown of a pumping station. In this context, the use of drag reducers may be a suitable solution for decreasing power consumption in fluid transport pipelines of petroleum and derivatives. This paper presents a case study of the application of drag reducing agents in a Brazilian high-energy pipeline. It features five intermediate pumping stations and three withdrawal points along its nearly one thousand kilometer stretch. With the aid of a computer simulation software, it is proposed a methodology to evaluate the best application condition, minimizing pump costs, polymer volume and meeting the scheduled demand of the month. This methodology first sought to validate the computational model of the pipeline. It was made a historical survey and inserted into the simulator, in order to reproduce faithfully a monthly operation. A sensitivity analysis is performed to determine which pump stations are most relevant. It was established an initial concentration of polymer to be injected in the sending refinery, aiming the reduction of arrangement or total shutdown of the subsequent station and keeping volume delivered on all points. The other bases remain working according to the operation of the month. This procedure is then repeated for the other bases, resulting in a combined and continuous injection, minimizing the operating costs. An economic evaluation is finally performed to quantify the benefits of this application. A reduction in energy consumption of 49% was noticed, and considering the costs with DRA, the monthly movement had a 35% drop in the total costs of operation.