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Heat exchanger network (HEN) synthesis has been acknowledged as an effective design technique to achieve significant energy savings in a wide range of industrial and engineering applications. In this regard, heuristic methods have been demonstrated as a powerful means to solve the HEN synthesis problem. However, because the new structure is generated at random, the structures that can effectively make the objective cost descend are limited in comparison to the entire evolutionary process. In addition, such heuristic methods are usually time-consuming processes. In this work, an intelligent search strategy is proposed to increase the efficiency of heuristic methods. Based on the defined objective cost, this strategy could rapidly and effectively find a route that could satisfy cost minimization. It can also achieve further evolution aimed at highlighting potential structures. The proposed search strategy can increase the ability of searching and reduce time consumption. Considering the random walk algorithm with compulsive evolution as an example, the principle and impact of intelligent search were clearly demonstrated. Furthermore, applying the intelligent search strategy to the random walk algorithm with compulsive evolution to some cases in the existing literature was found to yield better solutions.

Hydrogen (H2) is a clean fuel and key enabler of energy transition into green renewable sources and a method of achieving net-zero emissions by 2050. Underground H2storage (UHS) is a prominent method offering a permanent solution for a low-carbon economy to meet the global energy demand. However, UHS is a complex procedure where containment security, pore-scale scattering, and large-scale storage capacity can be influenced by H2contamination due to mixing with cushion gases and reservoir fluids. The literature lacks comprehensive investigations of existing thermodynamic models in calculating the accurate transport properties of H2-blend mixtures essential to the efficient design of various H2storage processes. This work benchmarks cubic equations of state (EoSs), namely Peng–Robinson (PR) and Soave Redlich–Kwong (SRK) and their modifications by Boston–Mathias (PR-BM) and Schwartzentruber–Renon (SR-RK), for their reliability in predicting the thermophysical properties of binary and ternary H2-blend mixtures, including CH4, C2H6, C3H8, H2S, H2O, CO2, CO, and N2, in addition to Helmholtz-energy-based EoSs (i.e., PC-SAFT and GERG2008). The benchmarked models are regressed against the experimental data for vapor–liquid equilibrium (VLE) that covers a wide range of pressures (0.01 to 101 MPa), temperatures (92 K to 367 K), and mole fractions (0.001 to 0.90) of H2. The novelty of this work is in benchmarking and optimizing the parameters of the mentioned EoSs to study VLE envelopes, densities, and other critical transport properties, such as heat capacity and the Joule–Thomson coefficient of H2mixtures in a wide range of associated conditions. The results highlight the significant effect of the temperature-dependent binary interaction parameters on the calculations of thermophysical properties. The SR-RK EoS demonstrated the highest agreement with VLE data among the cubic EoSs with a low root mean square error and absolute average deviation. The PC-SAFT VLE models demonstrated results comparable to the SR-RK. The sensitivity analysis highlighted the high influence of impurity on changing the thermophysical behavior of H2-blend streams during the H2storage process.

Natural gas hydrate (NGH), as a clean energy with great development potential, has been limited by the lack of efficient and safe exploitation methods for a long time. The CO2 displacement method proposed in recent years has not made substantial progress because of its poor permeability and low efficiency. The NH3 replacement method is proposed in this paper because of its strong permeability. We performed orthogonal molecular dynamics simulation of the replacement of CH4 in hydrate with NH3 and CO2 at different temperatures (245 K, 255 K, 265 K) and pressures (3 MPa, 5 MPa, 10 MPa). It is shown that:(1) Compared with CO2 molecules, NH3 and can penetrate into the hydrate layer effectively, and the resulting pore channels are conducive to the outward diffusion of CH4 molecules. (2) With the progress of the replacement process, the hydrate structure gradually decomposes but not completely, and the residual structure will inhibit the diffusion of CH4 molecules and lead to the agglomeration of CH4 molecules. (3) Within 1000 ps, the number of CH4 molecules replaced by NH3 is more than that of CO2 under the conditions of 245 K and 255 K, and less than that of CO2 under the conditions of 265 K. At the same temperature, the pressure does not affect the final comparison result.

Hydrogen exhibits interesting behavior that has intrigued many researchers in the last few years. It also provides a promising option for reducing carbon dioxide emissions but requires large storage as an energy carrier. In this study, we determine the safe pressure for hydrogen storage in a gas reservoir, so the fugitive and odorless molecules do not leak from the structural trap. For this reason, we propose a relation for estimating the safe pressure and quantify its uncertainty using Monte Carlo simulation. The uncertainty quantification is crucial because of the limited information available in this evolving field. We also adopt an Artificial Neural Network (ANN) approach to present the interfacial tensions of hydrogen systems for various pressures and temperatures. This study applies the proposed relation to the Ann Mag field near Corpus Christi in Texas and discusses complexities that arise in practice. It shows that the structural trap can sustain hydrogen pressure up to 8,438 psi at 10,239 ft and 10,515 psi at 12,020 ft below the surface. Higher pressures may lead to leakage because of fault slippage or fracture propagation. This study presents the heat maps of the interfacial tensions that are convenient tools for analyzing hydrogen transport. The proposed relation also has applications in the safe storage of hydrogen in geological formations.

Permian formation reservoir in the Sichuan Basin has the characteristics of high temperature(153 °C), strong heterogeneity, thin thickness, low porosity and low permeability(0.8 mD). In the early stage, gelling acid fracturing was mainly used to increase gas production, but the effect was not good, and the average daily production of gas wells after fracturing was 20000–30000 m3. The acid fracturing operation of Permian formation reservoir is faced with the problems of fast acid rock reaction speed under high temperature and low conductivity of acid etching fracture under high closing pressure. In view of the above technical problems, we carried out the visual test experiment of alternating injection acid fracturing. The fingering behavior of steering acid, gelling acid, and fracturing fluid is evaluated through experiments, and gelling acid is polymer gels. Through the experiment, it is clear that in order to achieve a good acid fingering phenomenon, the viscosity ratio should be increased to 20:1, the injection displacement is 50–70 ml/min, the controlled injection series are level 4–5, and the proportion of design acid dosage is 0.3. It can be seen from the 3D rock scanning diagram that with the increase of injection stages, the corrosion ability of acid to fractures is significantly enhanced. Multi-stage alternating injection acid fracturing technology has achieved good application effect in Permian formation of Sichuan Basin. The average gas production of the wells is 1.5 times higher than that of the gelling acid fracturing technology.

Based on laser-diffraction technique, the maximum entropy technique is applied to measure particle number concentration and size distribution in pneumatic transport. The information of particle number concentration and size distribution can be obtained simultaneously without distort the fluid and the particle trajectories. Detailed information about the distribution of the particle size and concentration in gas-solid two-phase flows near sand-bed surface are obtained in a large-scale wind tunnel test system. The experimental result shows that particle number concentration increases linearly with the increase of the wind speed. The mean particle size decreases exponentially with height (H > 3 cm), but almost unchanged for H < 3 cm. The weakly relationship of fluctuating velocity and particle-size dispersion is deduced from the underlying mechanism of equilibrium transport. This illustrates that transportation close to the mobile bed has selectivity for particle size.

Spontaneous imbibition behavior of fracturing fluid retained in reservoirs is considered to be one of the important mechanisms of spontaneous oil–water displacement in tight reservoirs. However, the mechanism of imbibition displacement in oil-wet rocks remains unclear. Inspired by the principle of the Marangoni effect, a mathematical model of oil displacement driven by an interfacial tension gradient in an oil-wet capillary tube was established, and the effects of osmotic pressure and hydraulic mechanical dispersion were coupled in the model. The results show that the key component of this mechanism is the breakdown of the initial capillary equilibrium state due to the diffusion of solute, which changes the pressure distribution in the capillary and drives the water to drive oil out of the pores. This can explain the spontaneous imbibition of low-salinity fracturing fluid into the oil-wet tight sample and displace oil without any external force, despite its low permeability. In addition, the related factors were analyzed, including initial concentration, contact angle, solute type, osmotic behavior, and size of the capillary. The results show that for an oil wet reservoir with high salinity, the connate water interfacial tension gradient plays an important role in oil displacement, and the coupling effect of osmotic pressure and hydromechanical dispersion considerably improves recovery. This can provide new insight into the mechanism of spontaneous imbibition of fracturing fluid.