Search

Wormlike micelles (WLMs), which are the self-assemblies of amphiphilic surfactants, have gained attention in various applications due to their unique ‘living’ nature (i.e., union-scission). However, the industrial production of the WLMs has rarely been discussed. Moreover, given that the system hardly reaches the desired viscosity without appropriate control actions within a reasonable operation time, a model-based controller has been implemented to the production of WLMs where CTAB/NaCl in a semi-batch reactor is used as a case study. For this, a high-fidelity model was developed by integrating thermodynamic, kinetic, and rheology models and validated with experimental results. Subsequently, to alleviate the computational burden of such a high-fidelity model, the model reduction was carried out by the multivariable output error state space algorithm. The developed reduced-order model (ROM) was able to reproduce the essential features of the high-fidelity model and utilized in the formulated model predictive control (MPC) system. Herein, the manipulated variables (i.e., surfactant volume fraction, salt concentration, and temperature) are adjusted to give the desired WLM length of 3700 nm (i.e., a zero-shear viscosity of 100 Pa-s). Finally, the developed MPC scheme resulted in optimal input sequences to successfully reach the setpoint within practical constraints.

In this study, we introduce an approach that applies machine learning (ML) in various procedures to predict graphene growth pattern in chemical vapor deposition (CVD) system. At first, CVD experiments were conducted to synthesize graphene using CH4 as a precursor on a Cu substrate at high temperatures, to get experimental data for training those models. Then, the size, coverage, domain density, and aspect ratio of graphene, which vary depending on the synthesis conditions, were measured and analyzed automatically by developing a region proposal convolutional neural network (R-CNN). Subsequently, an artificial neural network (ANN) and a support vector machine (SVM) were used to develop surrogate models to deduce the correlation between CVD process variables and the measured specifications. Characteristic graphene grains with hexagonal morphology were created using a generative adversarial network (GAN) to imitate the CVD growth. Then, they were modified to have the same size and aspect ratio as the predicted values and placed to meet the predicted coverage and domain density. Finally, the pre-generated images were modified using Pix2pix to obtain the same outlook as the experimental SEM images. As a result, it was possible to simulate graphene synthesis under various CVD condition. Through numerous simulations in advance, we were able to identify the experiment condition to synthesize graphene with the desired morphologies of large grain size and low domain density. Developing a platform to predict a CVD system for the controlled synthesis of graphene allow us to synthesize the graphene with high efficiency, saving tremendous amounts of time and expenses.

Offshore plants have many inherent constraints and risks compared to onshore plants, and it is crucial to optimize the operation of the offshore plant by adhering to constraints. Previous studies proposed a framework of utility systems that operate under various conditions. However, few studies have addressed the optimization of utility systems during the operating life cycle. To fill in this gap, we propose a new methodology for the design and optimization of the utility systems in the offshore plants. The utility systems are designed and optimized in Aspen Utilities Planner, considering the full life cycle of the oil and gas wells. For this, we first developed steady-state models of the topside processes based on every feasible operating scenario throughout the full life cycle. Then the utility consumption data was extracted from the simulation results and analyzed. The power system was designed using Aspen Utilities Planner (AUP), and it was optimized to maximize the thermal efficiency of the utility system, satisfying power demand for all the scenarios. The optimized results illustrate the significant saving of the operating cost and reduction of CO2 emission rate. Our studies suggest a generic framework to design utility systems in offshore plants.

This study introduces a new approach for designing a flare network system that ensures economic feasibility and safety using dynamic simulation analysis with the gPROMS ProcessBuilder software. A case of “separator outlet blocked discharge” was selected as a pressure-relief scenario based on the American Petroleum Institute Standard 521, and design data from a previous offshore project were used. As the main process and flare network system were dynamically simulated, the effects of the pressure, temperature, and liquid-level changes in the vessel on the opening of the pressure safety valve (PSV) were analyzed. Then, dynamic simulation results, including those for the flare load, PSV back pressure, and Mach number, were compared with those obtained from a steady-state model employing the Aspen Flare System Analyzer. Lastly, the sizes of the PSVs, branch lines, and main headers were optimized to minimize the overall capital costs and ensure the safety of the flare network system. This methodology can be applied to all existing and newly designed flare network system to enhance safety and reduce capital costs.

s A submerged combustion vaporizer (SCV) is normally used at a liquefied natural gas (LNG) terminal to vaporize LNG and supply natural gas to domestic and industrial markets. In this study, a new methodology was developed to reduce NOx efficiently in the flue gas of the SCV. The experimental setting of the SCV was prepared, and the impact of various process variables (e.g., excess O2 concentration, temperature, pH of water, and H2O2 concentration) on NOx reduction was investigated. For this, a surrogate model was developed by using an artificial neural network (ANN) algorithm based on the experimental data. The contributions of the input variables on NOx removal were assessed using sensitivity analysis. A genetic algorithm (GA) was integrated with the model to optimize the variables to maximize the removal of NOx in the flue gas. Finally, the ANN-GA model was validated through experimental verification, performed under the identified optimum condition.

In this work, response surface methodology and artificial neural network were adopted to build a model of a NOx removal system in a LNG terminal that estimates the released amounts of NOx in flue gas from a submerged combustion vaporizer. A small-scale SCV setting was prepared for the experiment, and it was operated under various conditions (i.e., changes of residence time, flow rate of the air, water temperature, and pH of the water). The simulation results demonstrated good agreement between both models and the experimental data, although the ANN model showed a higher accuracy than that of the RSM model.

Phages, plasmids, and other mobile genetic elements express inhibitors of CRISPR-Cas immune systems, known as anti-CRISPR proteins, to protect themselves from targeted destruction. These anti-CRISPRs have been shown to function through very diverse mechanisms. In this work we investigate the activity of an anti-CRISPR isolated from a prophage in Haemophilus parainfluenzae that blocks CRISPR-Cas9 DNA cleavage activity. We determine the three-dimensional crystal struture of AcrIIC4 and show that it binds to the Cas9 Recognition Domain. This binding does not prevent the Cas9-anti-CRISPR complex from interacting with target DNA but does inhibit DNA cleavage. AcrIIC4 likely acts by blocking the conformational changes that allow the HNH and RuvC endonuclease domains to contact the DNA sites to be nicked.

Scan data of urban environments often include representations of dynamic objects, such as vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud map with sequential accumulations of the scan data, the dynamic objects often leave unwanted traces in the map. These traces of dynamic objects act as obstacles and thus impede mobile vehicles from achieving good localization and navigation performances. To tackle the problem, this paper presents a novel static map building method called ERASOR, Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal, which is fast and robust to motion ambiguity. Our approach directs its attention to the nature of most dynamic objects in urban environments being inevitably in contact with the ground. Accordingly, we propose the novel concept called pseudo occupancy to express the occupancy of unit space and then discriminate spaces of varying occupancy. Finally, Region-wise Ground Plane Fitting (R-GPF) is adopted to distinguish static points from dynamic points within the candidate bins that potentially contain dynamic points. As experimentally verified on SemanticKITTI, our proposed method yields promising performance against state-of-the-art methods overcoming the limitations of existing ray tracing-based and visibility-based methods., Comment: 9 pages, 9 figures, RA-L with ICRA 2021 accepted

In this study, a 3D Normal Distributions Transform (NDT)-based pose correction framework for a mobile robot is proposed to prove its robustness under a considerable odometry uncertainty. Our proposed method consists of Extended Kalan Filter based on IMU and wheel odometry to estimate initial pose and NDT that corrects the initial guess via 3D scans. Then, the final pose correction is retrieved with Kalman gain using corrected poses from both EKF and NDT, followed by point cloud accumulation that generates the global map. Before examining our proposed method, quantitative comparisons of NDT over other 3D scan matching algorithms targeted for mobile robot pose correction were conducted. After that, the experiments are conducted for both indoor environments and outdoor environments. As verified experimentally, NDT is a) suitable for real-time pose correction over other 3D-scan matching algorithms b) robust even when loop closing is not available and c) shows better performance than EKF on pose correction under very imprecise raw odometry. Additionally, we analyzed the time-wise performance of NDT during registration over different voxel sizes of a global map.

The oxidative dehydrogenation (ODH) of butene has been recently developed as a viable alternative for the synthesis of 1,3-butadiene due to its advantages over other conventional methods. Various catalytic reactors for this process have been previously studied, albeit with a focus on lab-scale design. In this study, a multi-tubular reactor model for the butadiene synthesis via ODH of butene was developed using computational fluid dynamics (CFD). For this, the 3D multi-tubular model, which combines complex reaction kinetics with a shell-side coolant fluid over a series of individual reactor tubes, was generated using OpenFOAM&reg, Then, the developed model was validated and analyzed with the experimental results, which gave a maximum error of 7.5%. Finally, parametric studies were conducted to evaluate the effect of thermodynamic conditions (isothermal, non-isothermal and adiabatic), feed temperature, and gas velocity on reactor performance. The results showed the formation of a hotspot at the reactor exit, which necessitates an efficient temperature control at that section of the reactor. It was also found that as the temperature increased, the conversion and yield increased whilst the selectivity decreased. The converse was found for increasing velocities.

The constant selective pressure exerted by phages, the viruses that infect bacteria, has led to the evolution of a wide range of anti-phage defenses. One of these defense mechanisms, CRISPR-Cas, provides an adaptive immune system to battle phage infection and inhibit horizontal gene transfer by plasmids, transposons, and other mobile genetic elements. Although CRISPR-Cas systems are widespread in bacteria and archaea, they appear to have minimal long-term evolutionary effects with respect to limiting horizontal gene transfer. One factor that may contribute to this may be the presence of potent inhibitors of CRISPR-Cas systems, known as anti-CRISPR proteins. Forty unique families of anti-CRISPR proteins have been described to date. These inhibitors, which are active against both Class 1 and 2 CRISPR-Cas systems, have a wide range of mechanisms of activity. Studies of these proteins have provided important insight into the evolutionary arms race between bacteria and phages, and have contributed to the development of biotechnological tools that can be harnessed for control of CRISPR-Cas genome editing.

The arms race between bacteria and the phages that infect them drives the continual evolution of diverse anti-phage defences. Previously described anti-phage systems have highly varied defence mechanisms1-11; however, all mechanisms rely on protein components to mediate defence. Here we report a chemical anti-phage defence system that is widespread in Streptomyces. We show that three naturally produced molecules that insert into DNA are able to block phage replication, whereas molecules that target DNA by other mechanisms do not. Because double-stranded DNA phages are the most numerous group in the biosphere and the production of secondary metabolites by bacteria is ubiquitous12, this mechanism of anti-phage defence probably has a major evolutionary role in shaping bacterial communities.

In this paper, Ti/SnO2-Sb electrodes with different Sb dopant amounts were successfully fabricated on acid-treated Ti substrates by a thermal decomposition method; this was done in an attempt to examine the effect of the doping amount on the electrochemical activity and stability of the electrode. The physicochemical properties of the prepared samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM)-energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS). The electrochemical properties were investigated by linear sweep voltammetry (LSV), and the stability was studied via accelerated lifetime testing. The electro-catalytic activity of the prepared Ti/SnO2-Sb electrodes was strongly dependent on the amount of the Sb dopant, leading to a change in the OER overpotential. Among the prepared samples, the Ti/SnO2 electrode with 10% of a Sb dopant showed the highest catalytic activity for oxygen evolution reaction (OER), and 87% of COD removal with 20 mA cm−2 of current density for 60 min was achieved in the presence of 10.0 g L−1 of chloride. The findings reported herein suggest that the Ti/SnO2 electrode with an optimized Sb dopant amount shows enhanced electro-catalytic performance by easily generating active hydroxyl radicals, which act as strong oxidizing agents.

Catalytic reactors have been essential for chemical engineering process, and different designs of reactors in multi-scales have been previously studied. Computational fluid dynamics (CFD) utilized in reactor designs have been gaining interest due to its cost-effective advantage in designing the actual reactors before its construction. In this work, butadiene synthesis via oxidative dehydrogenation (ODH) of n-butene using tubular reactor was used as a case study in the CFD model. The effects of coolant and reactor diameter were investigated in assessing the reactor performance. Based on the results of the CFD model, the conversion and selectivity were 86.5% and 59.5% respectively in a fixed bed reactor under adiabatic condition. When coolants were used in a tubular reactor, reactor temperature profiles showed that solar salt had lower temperature gradients inside the reactor than the cooling water. Furthermore, higher conversion (90.9%) and selectivity (90.5%) were observed for solar salt as compared to the cooling water (88.4% for conversion and 86.3% for selectivity). Meanwhile, reducing the reactor diameter resulted in smaller temperature gradients with higher conversion and selectivity.

The increasing demand for liquid hydrogen as an environmentally friendly energy source necessitates developing an effective hydrogen liquefaction process. In this study, hydrogen production (steam methane reforming), hydrogen liquefaction, and CO2 liquefaction processes were successfully integrated by conducting several parametric studies involving three small-scale models and six scaled-up models. The enhanced liquefaction process included a modified Claude cycle that reduced the specific energy consumption to 6.15 kWh/kgLH2, 47% lower than that of a reference process. Three different refrigerants (nitrogen, liquified natural gas, and hydrogen) were utilized in the integrated process, and an interactive genetic algorithm-based multi-objective optimization model was developed using the MATLAB–Aspen HYSYS interface to minimize the energy consumption and CO2 generation. In addition, a hybrid non-isothermal ortho–para hydrogen cooling heat exchanger was designed by performing an H2 specific heat correction to enhance the robustness of the developed model. Techno-economic analysis of the designed process indicated that the integrated system considerably improved the main economic indicators (Levelized cost of hydrogen) and environmental friendliness of the hydrogen production process by approximately 51% and 95%, respectively. Moreover, CO2 liquefaction was performed most efficiently at a pressure of 120 bar with a reduced overall process cost. In addition, the LOCH (Levelized cost of hydrogen) of all the design integrated processes is less than $3/kg in market accessibility compared to the reference process. The sensitivity analysis results conducted by varying the costs of refrigerants, power (electricity), the selling price of liquid hydrogen, and carbon price revealed that the proposed integrated process had a higher application potential than other commonly used processes. In particular, under conditions of 45 tonne/day or more of carbon prices, the integrated process is environmentally and economically advantageous over the CO2 emission process with a reduced PBP of at least 10%.

s Membrane processes are typically analyzed to determine the most energy efficient and cost effective configurations and operating conditions. This can be achieved through simulation and optimization to identify the optimal designs for each case. In this study, a new approach is suggested for the visual analysis and comparison of the economic performance of different membrane configurations. The new methodology generates response surfaces for each configuration that can be used to show ranges of possible solutions. These solution surfaces can then be compared to show the conditions, under which each configuration become beneficial. This is demonstrated here through a case study using hollow fiber membrane separation systems to reduce the concentrations of CO 2 in natural gas.

This study introduces a new approach for the conceptual design of an offshore topside process, satisfying environmental standards, saving utility consumption, and consequently, maximizing economic profit. Twelve individual processes are modeled as a case study, based on sets of combinations between four topside process configurations and three individual production scenarios (i.e., peak oil, peak gas, and peak water) over the life cycle of an oil reservoir. Then, the simulation results of these models are analyzed based on economic profit. In particular, the simulation program is integrated with a mixed-integer non-linear programming algorithm to optimize the design and operating variables (e.g., operating pressures of the multi-stage separators) in order to maximize the economic profit of the platform. Lastly, an economic feasibility study is performed for the design of a profitable and eco-friendly offshore platform.