Compression Costs Research Articles

Development of a technology for the production of aluminum-nickel calcium catalyst for steam conversion of natural gas

A technology has been developed for preparing a catalyst for primary reforming of natural gas of complex geometric shape, cylindrical with one hole and external grooves, by impregnating the support with a solution of nickel nitrate and aluminum nitrate at a ratio of NCl(NO3)2: Al (NO3)3 = 3: 1. The carrier was obtained from mixtures consisting of alumina, calcium hydroxide with the addition of wood flour or graphite. The residual methane content at 7000°, the space velocity of 6000 g-1, the ratio of gas = (2.0 ÷ 2.2): 1 does not exceed 5–7%, and the mechanical crushing strength along the tertz is at least 18 MPa. The decrease in hydraulic resistance in the catalyst bed and the simultaneous increase in the geometric and specific surface of the catalyst granules can dramatically increase the performance of the units or while maintaining the gas load, reduce the cost of compression, significantly increase the service life of the reaction tubes. The GIAP-16 catalyst is more refractory than the GIAP-5 and 2 grades are produced: Grade A based on aluminum oxide and Grade B based on aluminum hydroxide. Production technology is reduced to the following operations.

Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty

Pipelines are one of the most efficient methods to transport large quantities of natural gas from gas reserves to main markets. However, because of the unsteady nature of gas flow, the operation of pipelines is always dynamic. Furthermore, the uncertainties in demand and the fluctuation of gas composition also make the efficient operation of these dynamic processes challenging. This study addresses the problem of determining the optimal operation to minimize compression costs, while considering demand and gas composition uncertainties. A dynamic pipeline network model is developed with rigorous thermodynamic equations, allowing accurate calculation of gas compressibility factor at any temporal and spatial point. The supply gas composition and demand nodes flow rates are assumed to be uncertain. To deal with these uncertainties, a robust optimization algorithm is applied using back-off constraints calculated from Monte Carlo simulation. Through successive iterations, the algorithm terminates at a solution that is robust to a specified level of process variability with minimal cost. We show from the case studies that the formulated model and the algorithm can successfully address the problem with acceptable computational cost.

Multi-Objective Gas Lift Optimization using Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II)

In petroleum industry, gas lift optimization is the most important for evaluating the reservoir. By improving the gas lift operation we can save money and time which we spend on the reservoir for effective production. The mainly accepted scenario of gas lift is to maximize production by using minimized cost infrastructure. If the production rate is increased, then the cost of oil production also increases due to the increase in surface facilities and increase in cost of gas compression to higher pressures. The production rate and production cost during gas lift are mutually conflicting in nature i.e., if anyone desires to increase the oil production rate, then at the same time it is difficult to minimize the cost of production. Therefore, this is an ideal candidate for multi-objective optimization study, where production rate needs to maximized while minimizing the cost of production. The oil production rate is calculated using nodal analysis of inflow performance and outflow performance curve while the production cost is calculated using the brake horsepower requirement of the compressor. Oil production rate during a gas lift operation can be defined as a function of various factors like (i) depth of gas injection, (ii) gas injection rate (iii) gas lift injection pressure, (iv) wellhead pressures, (v) bottom hole pressure, (vi) tubing size, (vii) surface choke size/wellhead pressure. Production cost mainly depends on the cost of gas compression which further depends on the pressure up to which gas has to be compressed in the annulus so that the gas lift valve at the bottom of the well opens. The opening of gas lift valve depends on the bottom hole pressure in the tubing i.e. the density of mixture present inside the tubing. The multi-objective gas lift optimization is carried out using multi-objective evolutionary algorithms (EAs) that use non-dominated sorting called elitist non-dominated sorting genetic algorithm (NSGA-II). In this project, we aim to find the optimum values of the decision parameters i.e. gas injection rate and wellhead pressure, for which oil production rate would be maximized while minimizing the cost of oil production.

Compression power requirements for Oxy-fuel CO2 streams in CCS

CO2 compression systems are commonly designed assuming negligible amount of impurities in CO2 fluid, it is of practical interest to evaluate the impact of impurities in oxy-fuel streams on the compression power requirements. Compared to more traditional postcombustion and pre-combustion capture methods, oxy-fuel technology produces a CO2 stream with relatively high concentration of impurities that may require partial or a high level of removal and whose presence can be expected to increase the costs of CO2 compression. Four types of compression technologies employed include four-stage compressor with 4 intercoolers, single-stage supersonic shockwave compressor, three-stage compressor combined with subcritical liquefaction and pumping and three-stage compressor combined with supercritical liquefaction and pumping. The study depicts that decrement of the impurities content from 15 to 0.7%v/v in the CO2 streams reduced the total compression power in the compression system. The study also concludes that three-stage compressor combined with subcritical liquefaction and pumping can potentially offer higher efficiency than four-stage compressor with 4 intercoolers for almost pure CO2 streams. In the case of raw oxy-fuel mixture, that carries relatively large amount of impurities, subcritical liquefaction proved to be less feasible, while supercritical liquefaction efficiency is only marginally lower than that in the four-stage compressor with 4 intercoolers.

OPTIMIZING ENERGY CONSUMPTION OF VEHICLE SENSOR NETWORKS BASED ON THE K-MEANS CLUSTERING METHOD AND ANT COLONY ALGORITHM

Abstract. With the world’s growing population, the number of vehicles has increased, but the capacity of roads and transportation systems has not. The increasing traffic and the pollution that it causes has therefore become a problem all over the world. Wireless Sensor Networks that detect traffic and prevent its pernicious effects have attracted the attention of many. Fast information transfers, easy installation, less repair and maintenance, compression and lower costs make WSNs more common than other network solutions (Nellore and Hancke 2016). Since more traffic congestion wastes time and energy, it is crucial to develop and present approaches to more accurately detect traffic patterns. Clustering is one of the best data analysis methods used for detecting traffic patterns. The approach proposed by this study assumes that all vehicles are equipped with GPS, and that sensors establish connections with each other through radio communication equipment. In this case, clustering is used to gather important traffic information and create intelligent urban transportation systems (ITS) to reduce sensor energy consumption in vehicular sensor networks. The K-means method and the ANT Colony optimization algorithm were used to cluster sensors and investigate their impact on reducing sensor energy consumption. The results show that the K-means algorithm and the ANT Colony algorithm reduce vehicular sensor energy consumption by 41.7 and 76.8 percent, respectively. The investigations showed that the ANT Colony algorithm outperformed the K-means algorithm by 84.2%.

Line‐pack storage in biogas infrastructures at regional scale, a model approach

Previously, we reported on a biogas transport model; the model assesses transport costs in a grid with fishbone and star layout collecting biogas. Biogas collection from several digesters to a hub supports the efficient use of resources. A dedicated grid, used for transport, can serve as a form of biogas storage as well. So a model was developed to evaluate line-pack storage in a transport grid for different digester scale, number of digesters, region size, and grid type. Line-pack storage does not require additional investments, and variable costs consist of extra compression costs. In both fishbone lay-out and star lay-out estimated line-pack storage, costs are between 0.3 and 1.5 euroct m(-3)h(-1). In a star lay-out, line-pack storage volume increases with region size. In a fishbone lay-out, the maximum line-pack storage volume is small in both a small size region and in a large region, as a result of pipeline volume and pressure restriction. A comparison of storage costs shows that line-pack storage can compete on costs with pressureless storage, but pressurized pipes are preferred for seasonal storage. A method to describe enlargement of line-pack storage by increased investment in pipelines depending on maximum transport pressure is presented. Such enlargement by applying larger pipe diameters could be financially sensible.

A 65-nm CMOS Lossless Bio-Signal Compression Circuit With 250 FemtoJoule Performance Per Bit.

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel 'xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the 'simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155um2. Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization.

Utilization potential of fly ash and copper tailings in concrete as partial replacement of cement along with life cycle assessment

Fly ash (FA) and copper tailings (CT) both are, anthropogenic wastes, spread all over the globe due to rapid growth in thermal power plants and progressive increase in the demand of copper. This study examines the feasibility of combined utilization of FA and CT in concrete as a partial replacement of cement by assessing compressive strength, cost, and environmental impact. Morphology and constituent minerals of FA and CT have been identified to understand the utilization potential. Subsequently, the concrete has been designed for 30 MPa target strength as per IS 10262:2009 for different mix proportions of FA and CT. Improvement (up to 8.27% compared to the control mix) in the compressive strength has been observed at combined replacement of 10% FA and 5% CT. The cost of concrete can also be reduced up to 16% without compromising its compressive strength. The environmental impact assessment of the modified concrete mix proportions has also been performed using life cycle assessment (LCA) as per ISO 14040:2006. Effect of all raw materials, electricity, and water consumption have been considered from their cradle to grave approach. One cubic meter concrete has been taken as a functional unit in LCA. Notable reduction has been observed in the chosen midpoint categories up to 38% in climate change, up to 32.6% in human toxicity, up to 33.6% in ozone depletion, up to 31.9% in agriculture land occupation, water depletion up to 34.3%, fossil depletion up to 34.8%, particulate matter up to 35.4%, and metal depletion up to 25.2%.

Solubility of Water in Hydrogen at High Pressures: A Molecular Simulation Study

Hydrogen is one of the most popular alternatives for energy storage. Because of its low volumetric energy density, hydrogen should be compressed for practical storage and transportation purposes. Recently, electrochemical hydrogen compressors (EHCs) have been developed that are capable of compressing hydrogen up to P = 1000 bar, and have the potential of reducing compression costs to 3 kWh/kg. As EHC compressed hydrogen is saturated with water, the maximum water content in gaseous hydrogen should meet the fuel requirements issued by the International Organization for Standardization (ISO) when refuelling fuel cell electric vehicles. The ISO 14687-2:2012 standard has limited the water concentration in hydrogen gas to 5 μmol water per mol hydrogen fuel mixture. Knowledge on the vapor liquid equilibrium of H2O-H2 mixtures is crucial for designing a method to remove H2O from compressed H2. To the best of our knowledge, the only experimental high pressure data (P > 300 bar) for the H2O-H2 phase coexistence is from 1927 [J. Am. Chem. Soc., 1927, 49, 65-78]. In this paper, we have used molecular simulation and thermodynamic modeling to study the phase coexistence of the H2O-H2 system for temperatures between T = 283 K and T = 423 K and pressures between P = 10 bar and P = 1000 bar. It is shown that the Peng-Robinson equation of state and the Soave Redlich-Kwong equation of state with van der Waals mixing rules fail to accurately predict the equilibrium coexistence compositions of the liquid and gas phase, with or without fitted binary interaction parameters. We have shown that the solubility of water in compressed hydrogen is adequately predicted using force-field-based molecular simulations. The modeling of phase coexistence of H2O-H2 mixtures will be improved by using polarizable models for water. In the Supporting Information, we present a detailed overview of available experimental vapor-liquid equilibrium and solubility data for the H2O-H2 system at high pressures.

Compression therapy for chronic oedema and venous leg ulcers: CoFlex TLC Calamine.

The prevalence of venous leg ulcers and chronic oedema is increasing because of the rise in the older population who have comorbidities. Managing and living with these conditions is extremely costly in resource and human terms and there is often a cyclical process of ulceration, healing and recurrence, resulting in significant physical and psychosocial morbidity. Identifying those at risk and advising on lifestyle changes to prevent progression of these conditions will help in avoiding high wound management and compression costs, nursing input and associated patient morbidity. Compression bandaging is the linchpin in managing these conditions and it must be started as early as possible. However, many patients find it difficult to tolerate bandaging because of issues such as pain, the inability to wear shoes and itch. Therefore, if compliance is to be achieved, it is important to select a compression bandaging system that addresses the issues that patients have difficulty with. AndoFlex TLC Calamine is a compression bandaging system that deals with many of these problems, and is easy to apply and remove. Testimonials by practitioners treating patients with chronic oedema, ulceration and/or skin problems will demonstrate the benefits and effectiveness of AndoFlex TLC Calamine.

The impact of the development of catalyst and reaction system of the methanol synthesis stage on the overall profitability of the entire plant: A techno-economic study

Abstract In this work, a detailed techno-economic assessment is carried out for methanol synthesis process that utilizes Cu/ZnO catalyst. First, a base-case plant is designed using a commercial methanol process. The economic feasibility study was developed to identify the potential performance metrics of the future catalytic systems and the boundaries upon which the catalyst could impact the profitability of the methanol process. In particular, the performance indicators investigated in this work look at the case of higher per-pass conversion (by introduction of higher efficiency catalysts), and the potential of utilizing low pressure reactors that could minimize compression costs. The results of this work indicated that synthesizing a catalyst with higher per-pass conversion has no significant effect on the process profitability. However, reducing the methanol synthesis reaction pressure could lead to a substantial improvement in the profitability of the plant. Insights obtained from this work can be used as the basis for identifying the characteristics of the future catalytic systems that have the potential to significantly decrease the costs associated with the conventional methanol synthesis plants.

Optimization of a 3-D isothermal plug-flow model of a monolith reactor featuring first order reactions

In this work, monolith reactor optimization is carried out. A 3-D monolith reactor model is first developed for a plug flow, isothermal, reactor featuring first order irreversible volumetric and surface reactions, then solved analytically using a series-based solution method, and finally optimized by establishing a number of mathematical model properties, and carrying out a broad search of the resulting reduced two-dimensional design parameter space. It is shown that maximum conversion is attained at a fluid velocity upper bound determined by the channel’s width, height, and capital cost to compression cost ratio. For reactors featuring only a surface reaction, maximum conversion is also attained at the maximum allowable channel width, and the minimum allowable channel hydraulic diameter. The optimum reactor residence time is inversely proportional (proportional) to the channel’s optimum hydraulic diameter (the reactor’s capital cost to production ratio).

Operational Experience of HiPACT Process

We would like to share some of the challenges faced during the first commercial use of the HiPACT® technology. HiPACT® is a technology jointly developed by JGC and BASF, which has now been successfully implemented by NIS, a Serbian multinational oil and gas company. HiPACT® is an advanced solvent based CO2 capture technology that contributes to improving the economics of Carbon dioxide Capture and Storage (CCS) in natural gas processing plants as well as Carbon dioxide Capture, Utilization and Storage (CCUS) projects such as Enhanced Oil Recovery (EOR). The solvent has excellent stability against thermal degradation, which enables operation at high pressure and elevated temperatures during solvent regeneration to reduce CO2 compression costs downstream. In addition, it also has a higher CO2 absorption capacity than other commercially available solvents which results in decreased solvent circulation rates and lower Acid Gas Removal Unit (AGRU) capital and operating costs. As the first commercial implementation, a HiPACT® unit was installed at the Elemir site which is owned by NIS with the aim of meeting the technical requirements of the national gas distribution network in Serbia. The natural gas was firstly introduced in January 2015 and continued for a trial run period until July 2015. The gas processing plant was then successfully commissioned and has been in commercial operation ever since this time. This paper presents i) operational experience with the HiPACT® unit and ii) economic evaluation including the compressor section to show the advantage of HiPACT® over state-of-the-art technology currently used worldwide.

Economic leverage affords post-combustion capture of 43% of carbon emissions: Supersonic separators for methanol hydrate inhibitor recovery from raw natural gas and CO2 drying

Offshore oil/gas productions are power intensive and CO2 emitters from gas-fired power generation. This work investigates supersonic separator as a strategy for affording post-combustion capture backed up by cost reductions. Conventional offshore gas processing usually loses thermodynamic hydrate inhibitor methanol in processing and exported gas. This work analyses a supersonic separator variant gas processing simultaneously reducing methanol losses. Such process dramatically improves gas-plant profitability via cost-reduction of methanol make-up and power-consumption, simultaneously increasing revenues from liquefied-petroleum-gas by-product. This economic leverage affords post-combustion carbon capture, including subsequent CO2 dehydration and compression for exportation of high-pressure liquid CO2. This corresponds to abate 43% of CO2 emissions boosting revenues via enhanced oil recovery. Moreover, CO2 is dehydrated via another supersonic separator operating with minimum head-loss, minimizing compression costs. Despite its much higher investment, the new process with carbon capture presents higher net value (865.63 MMUSD) than the conventional processing without carbon capture (829.31 MMUSD), being economically feasible and more environmentally adequate with cleaner natural gas production and successful CO2 management. The new process is superior in several scenarios and particularly favored by oil prices above 55 USD/bbl. Rising oil price from 40 to 100 USD/bbl, the new process net value rises 29%, whereas the conventional counterpart rises only 7.5%. In addition, as a plausible future scenario, CO2 taxation favors the new process, which always has superior economic performance, even without CO2 taxation. In summary, implementing supersonic separators in offshore natural gas processing aiming at anti-hydrate recovery and CO2 dehydration for enhanced oil recovery creates economic leverage sustaining Carbon Capture & Storage without loss of competitiveness. This result, backed up by rigorous thermodynamic simulations and economic-environmental assessments, configure an original achievement to the literature.

A linear time–cost tradeoff problem with multiple milestones under a comb graph

We consider a linear time–cost tradeoff problem (LTCTP) with multiple milestones under a comb graph. A time–cost tradeoff problem decides whether and how much to spend a cost to compress processing time of a job in order to meet promised due dates. This is common in managing a project because additional labors or resources are expensive. It is called linear where the compression cost linearly increases with reduced time. An objective of the LTCTP that this study addresses is to minimize the weighted number of tardy milestones plus the total compression cost. This study considers a special precedence structure, which is called a comb graph. A chain of jobs that sequentially precede each other forms a main process of a project, and other chains of jobs, corresponding to sub processes, precede each job in the main process. For this structure, we developed a strongly-polynomial-time algorithm. This is the first result of unveiling complexity of the multi-milestone LTCTP under a non-chain structure.

Dynamic Compressor Optimization in Natural Gas Pipeline Systems

The growing dependence of electric power systems on gas-fired generators to balance fluctuating and intermittent production by renewable energy sources has increased the variation and volume of flows withdrawn from natural gas transmission pipelines. Adapting pipeline operations to maintain efficiency and security under these dynamic conditions requires optimization methods that account for substantial intraday transients and can rapidly compute solutions in reaction to generator re-dispatch. Here, we present a computationally efficient method for minimizing gas compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flows. The optimization method uses a simplified representation of gas flow physics, provides a choice of discretization schemes in time and space, and exploits a two-stage approach to minimize energy costs and ensure smooth and physically meaningful solutions. The resulting large-scale NLPs are solved using an interior point method. The optimization scheme is validated by comparing the solutions with an integration of the dynamic equations using an adaptive timestepping differential equation solver, as well as a different, recently proposed optimal control scheme. The comparison shows that solutions to the discretized problem are feasible for the continuous problem and also practical from an operational standpoint. The results also indicate that our scheme produces at least an order of magnitude reduction in computation time relative to the state of the art and scales to large gas transmission networks with more than 6,000 kilometers of total pipeline. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0821 .

A linear time-cost tradeoff problem with multiple interim assessments within multiple projects in parallel

We consider a project scheduling problem in which the jobs can be compressed by using additional resources to meet the corresponding due dates. The project consists of multiple independent subprojects with completely ordered jobs. Some jobs have their own due dates for interim assessments of whole project. A penalty cost arises from the tardiness of a job, but it can be avoided through the compression of some jobs, which requires an additional cost. The objective is to minimize the total tardiness penalty and compression costs. We investigate optimality properties and develop an algorithm to find an optimal schedule in strongly polynomial time.

Modelling a bi-objective airport gate scheduling with controllable processing time using hybrid NSGA-II and VNS algorithm

In this research, we address a bi-objective model in a more realistic situation such that airport gate processing time is controllable. It is assumed that the possible compression/expansion processing time of a flight can be continuously controlled. The aim is simultaneously: 1) minimise the total cost of tardiness, earliness, delay and compression as well as expansion costs of job processing time; 2) minimise the passengers overcrowding on gate. In this study, a mixed-integer programming model is proposed. For solving the problem, two multi-objective meta-heuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and hybrid NSGA-II and variable neighbourhood search (VNS) are applied. VNS is used for preventing the solution from trapping in the local optimum, instead of mutation operator in NSGA-II. The algorithms are tested with the real life data from Mehrabad International Airport. Computational experiments reveal that hybrid NSGA-II and VNS generate better Pareto-optimal solution as compared to NSGA-II.

The algorithm for forecasting and gas compression cost optimization during underground gas storage exploitation

The algorithm for forecasting and gas compression cost optimization during underground gas storage exploitation

Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.