The demand for natural gas is rising globally due to its lower carbon emissions. Liquefied Natural Gas (LNG) is increasingly an important component of global energy trade. While the conventional LNG trade has been based on long-term contracts (LTCs), importers have sought greater flexibility in procurement, resulting in the development of the LNG spot market. Innovations in maritime transportation, such as the split delivery of LNG cargoes, provide even more operational flexibility for global LNG procurement. Previously, we proposed a mathematical programming model for the optimal procurement of LNG cargo through LTCs and the spot market, considering split deliveries. In this article, we propose a mathematical programming model that considers buying LNG by forming alliances or purchasing coalitions to minimize the total procurement cost incurred by importers. The proposed model is demonstrated using an illustrative example and a case study. Over a set of configurations, the proposed model saved $260.14 and $193.31 million over a planning horizon of 3 months through the formation of single and multiple purchase coalitions, respectively. These results clearly highlight that LNG procurement by forming purchase coalitions leads to significant cost savings, offering substantial benefits to importers.

With rapid material, process, and computation advances, traditional process simulators’ modelling, simulation, and optimization capabilities may not always suffice. Many process applications, research efforts, and tool developments demand integrating two or more independent, diverse, and disparate software platforms. Finding the details quickly for such an integration has always been a challenge faced by researchers, developers, and practitioners alike. This work presents a comprehensive tutorial for interfacing Python and Aspen HYSYS and achieving efficient information flow between them. The hierarchy of the Aspen HYSYS objects, their associated attributes, and built-in functions are discussed with examples. Strategies to handle unique objects (e.g., Column, Spreadsheet etc.) and backdoor variables are also presented. We further show how these ideas can be used for applications like technoeconomic assessment and simulation-based optimization. Lastly, we discuss how these ideas can be extended to MATLAB.

Electrochemical CO2 reduction (CO2R) in conventional systems typically generates highly diluted product output streams. This necessitates energy intensive and costly product separation, which potentially decreases the feasibility and economic viability of the process. Here, we describe the design and fabrication of a reversed gas diffusion electrode, which makes use of electrolyte pressure to channel products toward a collection chamber. Importantly, this strategy successfully excludes CO2 and permits gas products to be siphoned off at high purity. We further show that the electrolyte pressure and gas diffusion layer pore size are the key factors which govern the product collection efficiency. Using a nanoporous Au catalyst, we showcase the continuous production of high purity syngas over an extended 76 h period, operating at a full-cell energy efficiency of 37%. Importantly, we also demonstrate that this system is oxygen-tolerant, with no parasitic loss of current towards the oxygen reduction reaction even with a 95% CO2 + 5% O2 gas feed. Taken together, our results introduce a new design approach for CO2R electrolyzer systems.

Higher alcohols, such as n-butanol, are promising products of CO2 utilization due to their potential uses as energy carriers or chemicals. However, their direct formation from CO2 or via syngas is hindered by low yields. Here, we designed a novel 3-step process to produce n-butanol from CO2 via syngas and ethanol. Design strategies for an ethanol-butanol-water mixture over a wide composition range have also been discussed in detail. Process performance investigation revealed that the conversion of syngas to ethanol had little effect on the total annualized cost (TAC) of n-butanol and CO2 utilization efficiency (CUE), but TAC decreased and CUE increased steadily with the increasing conversion of ethanol to n-butanol to $1739/t and 47%, respectively. Conversion to n-butanol should be at least 20% for a positive CUE and a bypass strategy around the n-butanol reactor was proposed to benefit from >40% conversion. At modest conversions in both reactors (30-40%), the H-2 cost should fall to about $600-700/t for the proposed n-butanol process from CO2 utilization to be viable. Moreover, once 30-40% Guerbet conversion is achieved, further catalysis development should focus on improving selectivity.

Methylcyclohexane (MCH) is a promising organic hydride carrier for hydrogen transport and storage. Recovering hydrogen from MCH is an energy intensive process. An innovative idea of integrating this process with liquified natural gas (LNG) regasification is proposed in this study and demonstrated via modelling and simulation to substantially reduce external energy use. The synergistic benefits are twofold. In addition to providing a cold energy source for an effective high recovery cryogenic flash separation, the organic Rankine cycle based electrical power generation potential from LNG cold energy is fully exploited to reduce/eliminate external electricity demand for hydrogen compression to the high (end use) pressure. The results is a major reduction in carbon dioxide emissions. The proposed design for an integrated LNG-H-2 terminal can supply (1) 99.99 mol% pure hydrogen to a Combined Cycle Gas Turbine (CCGT) power plant and (2) commercial-grade natural gas to a gasgrid, both at the desired pressures and temperatures. Subsequently, rigorous simulation-based optimization was performed to minimize external energy inputs. A case study with 100 tph MCH and 100 tph LNG showed that integrating regasification with dehydrogenation produced 6.2 tph of hydrogen while gasifying LNG with a net power generation of 310 kW and a hydrogen recovery cost of 0.282 $/kg H-2. Up to 7.3 tonnes of hydrogen can be produced per 100 tonnes of LNG without the use of external power. On the other hand, using external power, up to 11.3 tonnes of hydrogen can be produced per 100 tonnes of LNG without any external refrigeration. Overall, the superstructure proposed in this manuscript provides a generic initial approach for future MCH hydrogen supply chain projects, when considering integrations with LNG regasification plants.

Hydrogen is believed to be a promising decarbonization vector. An optimal hydrogen supply-chain network achieving net-zero emissions in ASEAN by 2050 is determined. A logistic profile for decarbonization and declining costs with technological advances are assumed. It is found that the region has the potential to fully decarbonize its power sector with renewable energy if a regional power grid is installed. A mix of green (from surplus renewable power), blue (from natural gas) or orange (from biomass) hydrogen, the latter two with carbon capture and sequestration, meets industry, transport and power (without a regional power grid) sector demands. Results show that available biomass is insufficient to meet decarbonization targets beyond 2045 without green hydrogen. Prioritizing energy security and sustainability has minimal incremental costs compared to importing liquefied natural gas for blue hydrogen production. Optimal production locations are Myanmar, Cambodia for green hydrogen, and Indonesia, Vietnam for orange and blue hydrogen.

Even as global economies shift away from a fossil-based energy mix to renewables, in many countries, the share of natural gas (NG) in their energy baskets is increasing. As the cleaner option among fossil fuels and given its widespread availability at scale, NG is expected to play a dominant role as the bridge fuel during the energy transition. NG is traded globally under cryogenic conditions as liquefied natural gas (LNG). The LNG supply chain comprises LNG exports, maritime transportation, imports, regasification, and distribution to various end users. This work provides a comprehensive review of the scientific advancements related to the overall LNG supply chain. In the last two decades, major developments have occurred in terminal modeling; process and energy optimization; safety, risk, and consequence assessments; and supply chain (SC) optimization. Our review reveals that the management of boil-off gas continues to be a core challenge, as reflected in the broad range of studies to model, control, and optimize this aspect. We conclude our review with insights on opportunities for future research, including SC resilience and operational efficiency. Finally, new-generation fuels such as hydrogen and its carriers are also often handled under cryogenic conditions. Therefore, research on their SCs should be informed by the literature on LNG SCs.

Complex physical or numerical systems may exhibit distinct behaviors in various zones of their design spaces. We present an algorithm that uses multiple cluster-based surrogates for optimizing such box-constrained systems. It partitions the design space into multiple clusters using K-means clustering and develops a separate surrogate for each cluster. It then uses these surrogates to sample additional points in the design space whose function evaluations guide the search for a global optimum. Clustering, surrogate construction, and smart sampling are employed iteratively to add sample points until a pre-defined threshold. The best solution from these points estimates a global optimum. An extensive test bed of 52 box-constrained functions was used to evaluate and compare the algorithm's performance and computational requirements with sixteen derivative-free optimization solvers. The best version of our algorithm surpassed all sixteen solvers in optimization accuracy for a fixed number of evaluations and demanded lower computational effort than fifteen.

The international maritime organization has recently set targets and timelines for reducing global greenhouse gas emissions from the maritime industry, which currently stand at 1.1 Gt/year and make 3 % of the global emissions. Reducing emissions by increasing engine efficacy is the immediate target, but there is a limit to how much this path can achieve. Onboard post-combustion carbon capture and concentration in large marine vessels is emerging as an interim approach to reduce maritime emissions until large-scale deployment of low/zero emission fuels become viable. In this paper, we evaluate three carbon capture technologies (chemical absorption using either aqueous MEA or aqueous NH3 as the solvent, cryogenic separation, and membrane separation) for a medium-range tanker for two fuels, namely heavy fuel oil and liquefied natural gas. The capture cost per tonne of CO2 (recovery>90 %, purity>95 %) was considered as the assessment criterion, with simultaneous evaluation of other aspects such as energy and space demands. The simulations were carried out using MATLAB and ASPEN V12. For rate-based models, the adjustable parameters for the model were tuned using pilot plant data. Additionally, options for hot and cold energy integration were also assessed and implemented. Based on the reference ship conditions and assessment criteria, the simulation studies show that amine-based absorption is the best prospect for on-board capture by a clear margin.

Liquefied natural gas (LNG) is a key component in the global energy mix, offering a cleaner alternative to traditional fossil fuels. Managing boil-off gas (BOG) at export terminals, however, presents significant challenges. If not properly managed, BOG can compromise safety, waste resources, and pollute the environment. This study evaluates strategies for BOG management, including integrating jetty BOG into the fuel system, minimizing BOG generation through LNG subcooling, and BOG liquefaction. Detailed simulations and economic evaluations confirmed that all strategies are profitable. Liquefaction using recirculating LNG is the most effective, increasing LNG output by 1.62% and yielding an annual profit of $28.50 million. Other successful strategies include liquefaction in separate and existing cycles, which increase LNG by 1.38% and 1.36%, resulting in annual profits of $23.50 million and $23.84 million, respectively. Integrating the jetty BOG into the fuel system also results in a 0.63% increase in LNG, generating $11.01 million annually. Subcooling using a main cryogenic heat exchanger and nitrogen prestorage boosts LNG production by 0.53% and 0.45%, with profits of $9.25 million and $5.93 million, respectively. These strategies not only enhance LNG production and profitability but also optimize resource use and reduce environmental impacts, thus supporting broader sustainability goals.

Electrochemistry for producing pharmaceuticals has been gaining prominence in recent times as it offers a safer, greener, and cheaper alternative to conventional approaches for some key and difficult synthesis steps, such as trifluoromethylation. However, commercial application of the nanoparticle to a continuous manufacturing facility has many challenges. In this work, we develop 3D high-fidelity CFD models of various electrochemical reactor geometries for the trifluoromethylation of caffeine. The developed model is validated with in-house continuous flow trifluoromethylation experiments. The impact of the various process variables, such as the electrode gap, residence time, reactant inlet concentration, pulsation frequency, and duty cycle, on system performance is investigated in terms of yield, selectivity, and productivity. Several reactor configurations are analyzed, such as flat parallel plates, annular, serpentine channels, spiral, and 3D-printed electrodes. We find that the electrode gap and residence time greatly impact the system performance. Lower electrode gaps and longer residence times correlate to higher productivity. The 3D-printed electrode system was found to give a higher product yield compared with a flat electrode system. Furthermore, our CFD results show that employing spiral paths and serpentine channels offers a higher selectivity (up to 0.41) and enhanced productivity (increment of 23% compared with flat parallel plates).

Efficient CO2 capture onboard ships is a vital step in mitigating maritime emissions. In our previous work, we showed that amine-based absorption is the best prospect for onboard capture, and ships powered by LNG are better suited than those using HFO. Hence, in this work, an extensive design for amine-based absorption onboard LNG-run ships with different flue gas conditions (flow rate, temperature, and composition) as well as maximum CO2 storage capacity and number of days at sea is presented. The design comprises discussions on the selection of key variables for separation, e.g., the dimensions of the absorber and regenerator column and solvent flow rate, as well as the selection of optimal CO2 storage conditions. Additionally, the best configuration for cold energy integration to minimize the extra power demand for the CO2 compression is also assessed. The design is based on 90% recovery of CO2 from the total flue gas to be processed, including emissions stemming from extra fuel burned to fulfill the energy deficit for solvent regeneration and the power demand for CO2 compression. To this end, a novel noniterative approach to calculate total flue gas to be processed as a function of the flue gas conditions under optimized design conditions is also developed. Lastly, cargo losses from the installation of the capture unit are also presented. In summary, the study intends to provide ship owners with a comprehensive design guide for the installation of an amine-based absorption unit. To illustrate the utility of the study, case studies are presented using reference ships available in the literature.

As the world's energy demand grows, the LNG industry is gaining popularity as a cleaner fossil fuel source. Studies on process enhancement have proposed that retrofitting upfront nitrogen removal (UNR) technologies like the lithium cycle to the cold section, increases the process efficiency, and plant capacity and decreases power consumption. In this paper, an economic evaluation is conducted for varying percentages of UNR to study its profitability compared to the conventional cold section (base case) by calculating the grass-root capital expenditure (CGR) and cost of manufacturing (COM). The cost incurred to produce one million BTU LNG (COE) is used as a basis to compare the cases and calculate the increment in profit. While the techno-economic impact was studied for a range of UNR percentages, the specific results for 87.5 % UNR are as follows. The CGR and COM for the cold section alone decrease by 3.2% and 2.3% respectively compared to the base case. The CGR and COM of the lithium cycle increase linearly with the UNR percentage. The total annual cost (TAC) and LNG energy content increased by 12.3% and 4.5% respectively over the base process. This leads to a 7.4% COE increment over the base case for 87.5% UNR. The profit calculation for LNG price of 15$/MBTU showed a 4.27% or 101.74 million $/year increase in profit for 87.5% UNR case as compared to the base case.

Modeling is essential for designing, scaling up, controlling, and optimizing a reactor or process involving reactions. However, developing high-fidelity mechanistic models from first principles for reactor systems involving complex physiochemical phenomena is usually time- and resource-consuming. Therefore, machine learning models using data-driven methods can help in such cases to fill the gap between the complex system and our limited knowledge. Currently, most research works use generic off-the-shelf machine learning models to model reactor behavior. Such models frequently face problems related to data limitations, dynamics, model accuracy, and model interpretability. Considering the increasing need for data-driven models, especially in the fine chemicals and pharmaceutical industry, this work presents a new machine learning model architecture specially for the dynamic modeling of general flow reactors. Derived from the conventional residence time distribution reactor model, our generalized reactor neural ODE (GRxnODE) can achieve, without any prior knowledge of reaction kinetics, higher model accuracy, data efficiency, and model interpretability than commonly used data-driven models. The well-trained model can predict dynamic reactor response and learn reaction kinetics and reactor RTD from process data. The source codes of the model are publicly available.

Green hydrogen has been touted as the silver bullet for deep decarbonization. For green hydrogen to become a reality, its production must be economically competitive and practically scalable. Much effort is being devoted towards enhancing technologies such as water electrolysis, battery storage, and hydrogen storage. In this work, a detailed optimization model for designing a green hydrogen production facility with minimum landed cost of hydrogen is presented. The model is employed to study green hydrogen production in Saudi Arabia, Australia, Singapore, and Germany and to highlight the impact of geospatial solar irradiance on the facility design. The least production cost of $10.68 /kg-H2 occurs in Saudi Arabia based on the current technoeconomic landscape. The analyses highlight that storing hydrogen molecules in tanks is more economical than storing renewable electrons in batteries for producing green hydrogen. Grid-connected green hydrogen production facilities may yield lower hydrogen production cost but cannot guarantee carbon-free hydrogen. The sensitivity analyses with respect to a few technoeconomic factors highlight that massive reductions in solar panel and battery costs combined with low-interest loan incentives can make green hydrogen cost competitive. Overall, this work offers the research and industry communities a versatile and powerful tool to study the evolving technoeconomic landscape of green hydrogen production.

International Maritime Organization's (IMO) 2020 regulation was aimed to force the maritime industry to replace heavy fuel oil with cleaner and sustainable bunkering fuels. Liquefied natural gas (LNG) is a promising solution to achieving compliance with the established emission standards. However, its cryogenic nature demands new infrastructures and protocols for bunkering. Existing literature on LNG bunkering focuses primarily on protocols, standards, and safety. In this study, we present a comprehensive evaluation of the LNG bunkering procedure in the world's first national standard Technical Reference 56 (TR 56) using rigorous dynamic simulation. The bunkering time, material costs, and emissions for truck-to-ship and ship-to-ship bunkering are estimated. Bottom-filling operation is recommended for balancing the pressures of two tanks and managing boiler-off gas (BOG) efficiently, when a vapor return line is present. For leading maritime countries such as Norway, Hong Kong, and Singapore, purging and inerting the bunkering lines can impact emissions and material costs significantly.

Over the last five decades, there have been a few phases of interest in the so-called hydrogen economy, stemming from the need for either energy security enhancement or climate change mitigation. None of these phases has been successful in a major market development mainly due to the lack of cost competitiveness and partially due to technology readiness challenges. Nevertheless, a new phase has begun very recently, which despite holding original objectives has a new motivation to be fully green, based on renewable energy. This new movement has already initiated bipartisan cooperation of some energy importing countries and those with abundant renewable energy resources and supporting infrastructure. For example, the abundance of renewable resources and a stable economy of Australia can attract investments in building these green value chains with countries such as Singapore, South Korea, Japan, and those even further distant like in Europe. One key challenge in this context is the diversity of pathways for the (national and international) export of non-electricity renewable energy. This poses another challenge, i.e., the need for an agnostic tool for comparing various supply chain pathways fairly while considering various techno-economic factors such as renewable energy sources, hydrogen production and conversion technologies, transport, and destination markets, along with all associated uncertainties. This paper addresses the above challenge by introducing a probabilistic decision analysis cycle methodology for evaluating various renewable energy supply chain pathways based on the hydrogen vector. The decision support tool is generic and can accommodate any kind of renewable chemical and fuel supply chain option. As a case study, we have investigated eight supply chain options composed of two electrolysers (alkaline and membrane) and four carrier options (compressed hydrogen, liquefied hydrogen, methanol, and ammonia) for export from Australian ports to three destinations in Singapore, Japan, and Germany. The results clearly show the complexity of decision making induced by multiple factors. For the case study, under the given input parameters, the methanol combination with alkaline electrolysers becomes the least-cost supply chain option for Singapore, Japan, and Germany with expected levelised costs of hydrogen (ELCOH) of 6.53, 6.61, and 6.93 $/kgH2, respectively. However, the second-best choices are not the same for all countries. Ammonia (with alkaline electrolysers) becomes the second-best option for Singapore ($7.98/kgH2) and Japan ($8.20/kgH2) destinations, while methanol (this time with PEM electrolysers) proves to be the second-best supply chain option for German destinations ($8.62/kgH2).

Interest in low emissions hydrogen as an energy vector to assist in deep decarbonization goals has gained momentum recently. In this paper, we explore local hydrogen production from natural gas with CO2 capture and sequestration (known as “blue” H2) to support Singapore's intended inclusion of low-carbon intensity H2 fuel as a way to achieve significant CO2 emission reduction through 2050. A superstructure-based model is used to minimize the total cost or emissions of the entire supply chain, from H2 production to consumption in the power, industry, domestic, marine, and road transport sectors. H2 demand for each sector is projected for three H2 penetration scenarios (low, medium, and high). The results are analyzed for the levelized cost of H2, reduction in carbon emissions, and cost of carbon avoidance. Costs associated with direct and indirect CO2 emissions, as well as H2 and CO2 infrastructure costs, are included in total costs. A comparison of blue H2 and direct natural gas utilization with post-combustion CO2 capture and sequestration is also presented. We find that decarbonizing Singapore via local production of blue H2 will involve a relatively high carbon avoidance cost.

Many complex systems display distinctly different behaviors across regions, zones, or sub-domains. A single surrogate may not suffice in modelling such systems. A better approach would be to identify the various zones and model them individually. In this work, we propose a zone-wise surrogate modelling (ZSM) algorithm to identify various zones in a system's input domain based on a user-specified acceptable goodness of fit and recommend the best surrogate for each identified zone from a library of potential surrogates. We have assessed ZSM on ten case studies involving complex 1-D functions and compared its modelling performance against some non-linear and piecewise models. We also show how ZSM can help in global optimization using five complex multimodal functions and found that a ZSM-based approach successfully identifies the true global optima of these functions. In future, we aim to extend ZSM for the modelling and optimization of complex multi-input single-output (MISO) systems.

Chemical industry mainly uses fossil fuels as energy resources and thus causes substantial amount of carbon emissions. Combined heat and power (CHP) systems have become an effective solution for reducing energy consumption and carbon emissions. This paper presents a bi-objective mixed-integer linear programming framework for technoeconomic and environmental optimization of CHP systems for chemical plants, accounting for steam turbine network design and renewable integration. The bi-objective optimization problem is solved to achieve a trade-off between total annual cost (TAC) and carbon emissions. Two typical quad-pressure and dual-pressure chemical plants are employed for demonstration. The results show that the optimal CHP system has a TAC of 21.1 and 18.9 million USD as well as carbon emissions of 50 and 35 kton for quad-pressure and dual-pressure plants. Both systems feature a sharp reduction of 33.3% and 42.5% in emissions and a mild increase of 26.7% and 30.2% in TAC when comparing with TAC minimization. Moreover, we find that gas boiler is mainly responsible for carbon emissions, and replacing it with electric boilers is an promising alternative for further emission mitigation. We observe that photovoltaics capital price has a significant impact on system TAC and emissions, while employing only carbon tax is unable to achieve deep decarbonization. Hence, we recommend combining system design, renewable technology advances, and carbon tax synergistically for chemical industry decarbonization. Our methodology provides an effective and flexible framework for further investigation of decarbonization.