As the demand for offshore oil platforms and eco-friendly oil production has increased, it is necessary to determine the optimal conditions of offshore oil production platforms to increase profits and reduce costs as well as to prevent environmental pollution. The Reid vapor pressure (RVP) of product oil is a key specification as an environmental consideration. To achieve a practical design for an offshore platform, it is necessary to consider environmental specifications based on an integrated model describing all units concerned with oil and gas production. In addition, an effective optimization strategy is required to determine the design variables. In this study, using Aspen HYSYS and a stochastic optimization strategy for simulation and optimization, the design variables for the crude oil separation process were simultaneously determined to maximize profits. The results of this research show that the condensate recycling train has an excellent capability to adjust the vapor pressure of crude oil, and an increase in the separation stage results in an improvement in the process performance. It is also important to consider the trade-off between the oil production and operating costs because an increase in the oil production rate and a decrease in the RVP of product oil can be achieved by an additional expenditure in the operating costs. This study can facilitate the design of efficient and eco-friendly offshore platforms.

Dimethyl ether (DME) is a clean and efficient synthetic fuel with the potential to substitute liquefied petroleum gas (LPG) and diesel. From both the environmental and economic viewpoints, there is a strong preference to use biomass as the feedstock of synthetic fuels. In this study, state-of-the-art technologies were surveyed and a superstructure representation for the DME production process involving the use of biomass as a feedstock was investigated. Moreover, a mixed-integer nonlinear programming (MINLP) model was proposed for developing a DME production process based on biomass gasification. A combination of optimal technologies was adopted; detailed studies are presented to demonstrate the key features of the proposed superstructure.

In this work, we present a solution procedure for design of a chemical process for effectively adjusting calorific values in an offshore regasification terminal. To tackle the technical and commercial issue in the liquefied natural gas (LNG) industry caused by differences of LNG calorific values between importing countries, many methods and configurations are being studied. This design problem is defined in two parts: a generalized disjunctive programming (GDP) problem with one objective and a multiobjective problem for minimizing the operating costs and the performance of natural gas liquids (NGLs). First, the GDP problem has been mathematically reformulated as a mixed-integer nonlinear programming (MINLP) problems, and the MINLP technique incorporated into the process simulator using its own optimization capabilities has been suggested. For solving the resulting bicriterion problem with the MINLP problem, we have suggested the heuristic procedure that reduces the number of discrete solutions which are necessary for complete Pareto optimal sets. The complete Pareto optimal sets for a new calorific value adjustment process under three feedstock scenarios are generated.

Usually, risk assessment is a combination of risk analysis and risk appraisal to evaluate the consequences and frequencies of hazardous events. The acceptability of the exposed risk is also judged in the process. In this regard, the financial risk matrix could be a useful tool based on the frequency and expected loss of damage. Thus, it is adopted in this study where a financial approach has been taken in risk assessment; the proposed methodology uses frequencies from chemical accident records and value at risk (VaR). The methodology consists of mainly five steps. It starts with hazard identification, and is followed by adjustment of history-based accident frequencies by using severity ratios. Then, accident probability and expected damage loss are estimated. The results obtained in the previous two steps are combined to compute VaR for the target process; this value is mapped with financial risk matrix to re-evaluate accident frequency. As an illustrative case, Texas BP refinery accident in 2005 is studied according to the proposed methodology. The results indicate that the financial risk increased from a low level to a medium high level after the occurrence of the tragic accident. As similar accidents frequently occurred for the same process, the risk of the process should have been increased. This proposed method can reflect this dynamic change in risks with the help of accident records and their impacts. 

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