Techno-Economic Assessment of Fischer-Tropsch and Direct Methane To Methanol Processes In Modular GTL Technologies

In 2014, about 3.5% of the global gas production was flared, of which 0.289 TCF were in the US alone. This increase of natural gas flaring in the US has been exacerbated by the drilling and fracking activities in the shale gas plays. Improper flaring of natural gas leads to emissions of methane and other organic volatile compounds, sulfur oxides (SOX) and carbon dioxide (CO2). In fact, by 2020 the total gas volume flared is projected to be up to 60% greater than that in 2000, which is problematic. Thus, there is a great and pressing need for curbing or eliminating the flared natural gas and fugitive methane from remote reservoirs in order to protect the environment and avoid global warming. This study aims at investigating the potential use of the Fischer-Tropsch (F-T) synthesis process, in a microchannel reactor (MCR), and the Direct Methane to Methanol (DMTM) process in a compact plant footprint for curbing or eliminating natural gas flaring. The two processes were modeled using the process simulator Aspen HYSYS v7.2 and their operational and economic performances were evaluated in terms of the products yield, net present value (NPV), payback period (PBP) and internal rate of return (IRR). In addition, the effects of tailgas and methane recycle ratios on these process performances are investigated. The simulation results showed that the unit cost of the DMTM process was very sensitive to the methane recycle ratio, however, that of the F-T in MCR was less sensitive to the tail gas recycle ratios. In order to maintain an IRR > 10%, which is the minimal acceptable value, the tail gas recycle ratio for the F-T in MCR had to be greater than 8 and 30%, at CO conversions of 80% and 72%, respectively, whereas for the DMTM process, a minimum methane recycle ratio of 60% was required to achieve any profitability. In addition, the DMTM process appeared to have significantly higher net energy requirements per product yield when compared with those of the F-T in MCR process; however, both processes had higher energy requirements than those of conventional GTL technologies

Risk Assessment Strategies to Reduce Profitability Losses from Pipeline Accidents in the Natural Gas Industry

Ineffective risk assessment strategies can negatively impact the natural gas industry. Engineer project managers who struggle to maintain a risk assessment plan are at high risk of failure, which could result in devastating consequences for the business and environment. Grounded in the theory of risk assessment, the purpose of this qualitative single case study was to explore strategies engineer project managers in the natural gas industry use to improve risk assessment planning to reduce pipeline accidents and improve profitability. The participants comprised of 5 engineer project managers in Virginia, who effectively use risk assessment strategies to promote safety metrics and maximize effective approaches to improve the natural gas industry. Data were collected from semistructured interviews, company documents, and company social media platforms. Thematic analysis was used to analyze the data. Four themes emerged: safety, training and development, process management, and strategic risk assessment. The implications for positive social change include continuous monitoring of project engineer managers to create a risk assessment plan to support safety initiatives for economic development in the business, environment, community, and society

Social, environmental and economic impacts of alternative energy and fuel supply chains

Energy supply nowadays, being a vital element of a country’s development, has to independently meet diverse, sustainability criteria, be it economic, environmental and social. The main goal of the present research work is to present a methodological framework for the evaluation of alternative energy and fuel Supply Chains (SCs), consisting of a broad topology (representation) suggested, encompassing all the well-known energy and fuel SCs, under a unified scheme, a set of performance measures and indices as well as mathematical model development, formulated as Multi-objective Linear Programming with the extension of incorporating binary decisions as well (Multi-objective Mixed Integer-Linear programming). Basic characteristics of the current modelling approach include the adaptability of the model to be applied at different levels of energy SCs decisions, under different time frames and for multiple stakeholders. Model evaluation is carried for a set of Greek islands, located in the Aegean Archipelagos, examining both the existing energy supply options as well future, more sustainable Energy Supply Chains (ESCs) configurations. Results of the specific research work reveal the social and environmental costs which are underestimated under the traditional energy supply options' evaluation, as well as the benefits that may be produced from renewable energy based applications in terms of social security and employment

Advances in the Optimization of Energy Systems and Machine Learning Hyperparameters

Intensifying public concern about climate change risks has accelerated the push for more tangible action in the transition toward low-carbon or carbon-neutral energy. Concurrently, the energy industry is also undergoing a digital transformation with the explosion in available data and computational power. To address these challenges, systematic decision-making strategies are necessary to analyze the vast array of technology options and information sources while navigating this energy transition. In this work, mathematical optimization is utilized to answer some of the outstanding issues around designing cleaner processes from resources such as natural gas and renewables, operating the logistics of these energy systems, and statistical modeling from data. First, exploiting natural gas to produce lower emission liquid transportation fuels is investigated through an optimization-based process synthesis. This extends previous studies by incorporating chemical looping as an alternative syngas production method for the first time. Second, a similar process synthesis approach is implemented for the optimal design of a novel biomass-based process that coproduces ammonia and methanol, improving their production flexibility and profit margins. Next, operational difficulties with solar and wind energies due to their temporal intermittency and uneven geographical distribution are tackled with a supply chain optimization model and a clustering decomposition algorithm. The former describes power generation through energy carriers (hydrogen-rich chemicals) connecting resource-dense rural areas to resource-deficient urban centers. Results show the potential of energy carriers for long-term storage. The latter is developed to identify the appropriate number of representative time periods for approximating an optimization problem with time series data, instead of using a full time horizon. This algorithm is applied to the simultaneous design and scheduling of a renewable power system with battery storage. Finally, building machine learning models from data is commonly performed through k-fold cross-validation. From recasting this as a bilevel optimization, the exact solution to hyperparameter optimization is obtainable through parametric programming for machine learning models that are LP/QP. This extends previous results in statistics to a broader class of machine learning models