Refineries consume substantial energy and water while emitting significant CO2. Solvent-based Carbon Capture (CC) offers a viable CO2 mitigation solution but increases water and energy consumption and requires considerable investments. This research aims to overcome these barriers and improve the Iranian refining sector towards Sustainable Development Goals (SDGs) 6, 7, and 13 by mitigating CO2 emissions, producing low-carbon hydrogen, and utilizing unconventional water resources for CC plants. A comprehensive nexus approach was incorporated to account for the intricate interconnections between SDGs and refineries' water, energy, and CO2 networks. We evaluate the implementation of CO2 market regulations corresponding to seven international Emissions Trading Systems (ETSs) of the EU, UK, New Zealand (NZ), Regional Greenhouse Gas Initiative (RGGI), South Korea, California, and China in the Iranian refining sector to incentivize CC integration. The problem was formulated using Stochastic Multi-Objective Mixed-Integer Linear Programming optimization, with K-means Clustering applied to account for ETS CO2 price fluctuations and uncertainties. Results showed intense competition between economic and environmental objectives without ETS regulations, but appropriate ETS implementation could transform this conflict into a cooperative compromise. Under EU and UK scenarios, 60.3% and 67% of CO2 emissions could be avoided with fully covered capture costs, potentially leading to net economic profit through surplus allowance sales. Implementation of the other five ETSs could reduce capture costs by 18-67%, with a 16.4% CO2 avoidance.

A set of recommendations is one of the most valuable outputs of the hazard and operability (HAZOP) study. The HAZOP study team provides recommendations when deficiencies are detected in the chemical process plant. These deficiencies can cause chemical process accidents and operability issues. This study employed a data-driven approach using natural language processing (NLP) and machine learning (ML) to predict potential recommendations based on causes and consequences. The dataset had no label; thus, clustering was used to label it. Firstly, bidirectional encoder representations from transformers (BERT) converted recommendation sentences into vectors. Secondly, uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were utilized to determine recommendation categories and label the dataset. Then, BERT was used to convert causes and consequences into vectors. Finally, a multi-layer perceptron (MLP) classifier was employed to predict possible recommendations based on causes and consequences. The class imbalance problem was handled by random over-sampling. The prediction accuracy of possible recommendations based on causes and consequences equals 93.7% and 89.5%, respectively. As a result of predicting potential recommendations utilizing causes and consequences, major recommendations will not be overlooked during the HAZOP study. This can further expand NLP and ML applications in HAZOP study automation.

Natural or buoyant convection flow is an exemplary heat transfer phenomenon, with growing applications in various industries. This article develops a new algorithm, which models and solves the buoyancy-driven turbulent flows in enclosures more accurately than the past similar solvers. A careful literature review shows that the past existing approaches have mostly had serious limitations to apply their algorithms to buoyancy-driven flows with high temperature differences magnitude because of employing the classical Boussinesq approximation. As the novelty of this study, it benefits from a momentum-based variable approach in the context of the semi-implicit method for the pressure linked equations (SIMPLE) algorithm, which lets it accurately solve the strong compressible buoyant flows with high temperature differences. The algorithm is applied to both the Navier-Stokes and the accompanied turbulent flow governing equations using OpenFOAM 4.1 as the platform. To validate the developed algorithm, the current results are compared with experimental data in both square and tall cavities considering low (8.6 x 10(5)), high (1.43 x 10(6)), and very high (1.58 x 10(9)) Rayleigh numbers. As the major contribution of this work, it improves the accuracy of the thermo-buoyant turbulent flow prediction at both low and high Rayleigh numbers. All test cases are carried out employing two different turbulence models of k-omega and k-epsilon. Furthermore, comparing the results of the present non-Boussinesq algorithm and those of the past developed methods with the experimental data, it is shown that the present algorithm provides a more accurate prediction for the temperature field, that is, <10% differences with the experimental data. Moreover, the present maximum velocity results surpass the solution of the past numerical methods and show <3% differences with the experimental data.

Surfactant injection is a promising method for enhanced oil recovery (EOR) due to its effective micro-displacement mechanisms. However, understanding the interaction of a surfactant solution with heavy oil in porous media is neither straightforward nor well understood, particularly in heterogeneous systems. By enabling in-situ real-time monitoring of flow transport, microfluidic studies have provided novel insights into the underlying multiphase physics of flow at the pore scale. This paper examines the two-phase displacement efficiency of a new surfactant in layered-fractured porous microfluidic patterns, a topic seldom discussed in the literature. To evaluate the performance of the proposed surfactant, we considered several heterogeneous media with varying layer and fracture geometrical characteristics, quantifying displacement efficiency for each case. Based on the analysis of pore-scale snapshots, it was inferred that the primary mechanisms responsible for EOR during surfactant flooding into heavy oil include pore wall transportation, emulsifications, the deformation of residual oil, inter-pore or intra-pore bridging, and wettability alteration. Macroscopic displacement experiments revealed that the width of the swept area from surfactant injection significantly exceeded that of water injection, resulting in a substantially higher oil recovery. Furthermore, it was demonstrated that the direction of fluid flow in relation to fracture orientation plays a critical role in the dynamics of surfactant solution movement and, consequently, the ultimate oil production.

The HAZOP (Hazard and Operability) study is one of the most well-known approaches in process hazard analysis. The HAZOP study is a systematic procedure a multidisciplinary team uses to find hazards and operability issues through brainstorming. The conventional HAZOP study requires much time, is also knowledge-intensive, and is susceptible to human mistakes. Therefore, there is a significant incentive to automate the HAZOP study. This study investigated the effectiveness of Natural Language Processing (NLP) and Machine Learning (ML) in HAZOP study automation. The case study used in this contribution is based on a conventional HAZOP study report. Initially, the causes were converted into feature vectors using NLP's simple sentence embedding technique (Bag of Words). Random oversampling was employed to manage the limited and imbalanced dataset. Finally, ML classifiers such as Decision Tree, linear Support Vector Machine, Random Forest, Logistic Regression, Gaussian Naïve Bays, and K-Nearest Neighbors were applied to predict deviations. Decision Tree outperformed other classifiers with 92% accuracy. This study's integrative approach applies even to small units and companies with limited training datasets. This is because it does not require a large training dataset.

In this work, a new multi-class classification approach was employed in the QSAR model to assess chemical toxicity prediction through handling the imbalanced dataset as the critical preprocessing step in the training dataset. Various classifiers of the decision tree, K-NN, naive Bayes, kernelled naive Bayes, and SVM and two distinct acute aquatic toxicity datasets towards Daphnia Magna and Fathead Minnow Fish were used to evaluate the generality of the approach. The quantitative response (LC50) was discretized into ten bins. Imbalanced dataset classification leads to a high level of errors since the classifier tends to learn from the majority class more than the minority class. Each training dataset was specified by different weights related to the class population. These datasets were then bootstrapped based on their weights to convert the imbalanced dataset into a balanced one. This approach enhanced the accuracy of classification of material toxicity dramatically (up to 99%). Balanced dataset classification had high overall accuracy when correlated attributes were removed. Therefore, fewer attributes are sufficient to predict material toxicity.The overall accuracy improvement of the decision tree, K-NN, naive Bayes, kernelled naive Bayes, and SVM for the Daphnia Magna dataset after balancing the data set are 58.03%, 55.08%, 9.09%, 72.48%, and 53.05%, respectively.

In the present study, due to various advantages of process miniaturization, the integrated design and operation of a mobile power generation system consisting of a microreactor reformer and a proton exchange membrane fuel cell (PEMFC) are investigated. The hydrogen fuel is supplied through autothermal steam-reforming of methanol in a micro-reactor leading to a safer, as well as more efficient, and economical operation. A high-temperature polymer membrane fuel cell with external accessories is applied for power generation. Then, simulation-optimization programming is applied for simultaneously optimizing the process design and operation at the same level. The result shows that the implemented procedure ensures the economic and flexible operation of the process while satisfying the safety constraints. The maximum gross power and net power generation are 109.3 and 91.9, respectively, while the cost of hydrogen production reduces from 13 to 20 $/kg to 7.7 $/kg. The fuel (methanol) consumption can be as low as 0.42 L/kWh. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

In this paper, the pyrrolic nitrogen functional group's presence is investigated in the selective adsorption of CO2. The grand canonical Monte Carlo (GCMC), molecular dynamics (MD), and density functional theory (DFT) were used for this study's goal. GCMC was used to calculate adsorption isotherms, selectivity, and isosteric heats. MD was used to calculate the radial distribution function (RDF) and diffusion. Reactivity parameters, electrostatic potential (ESP), reduced density gradient (RDG), and independent gradient model (IGM) were calculated using DFT. Adsorption isotherms, selectivity, and isosteric heats demonstrated that the NH group caused the selective adsorption of CO2 among CH4, CO, and N-2 gases. The adsorption quantities for CO2, CH4, CO, and N-2 are 4.77, 2.65, 2.38, and 2.09 average loading (per cell), respectively, at 10 MPa and 298 K. Furthermore, the thermodynamic parameters and Henry's constant were calculated, indicating that CO2 adsorption is more dependent on temperature than that of other gases. RDF results showed an effective interaction between oxygen and carbon of CO2 and hydrogen and nitrogen of NH, respectively. Additionally, ESP results revealed that the NH group changed the electron's density in the molecule surface, causing a more vital interaction between CO2 and NH, confirming the RDG and IGM results.

As discussed extensively in the previous chapters, solid oxide fuel cell (SOFC) technologies offer significant advantages over conventional fuel cell counterparts in terms of fuel flexibility and higher operating temperatures that allow integration with a wider range of applications. However, such rewards come at the price of a more sophisticated design which makes SOFCs' operation more interactive and challenging. System failure could happen by a single component or an interactive cluster of components. SOFCs are tolerant of some reversible faults such as carbon deposition and minor, early, sulfur-poisoning. However, most mechanical failures (e.g., microstructure sintering) irreversibly damage the equipment and would require component replacement. Unfortunately such failure mechanisms do not exhibit unique diagnosable symptoms. Therefore fault diagnostics and isolation techniques necessitate further research. This chapter discusses fuel variability and extreme operating conditions that increase the risk of hazards associated with SOFC systems. A systematic approach to design and operation considering stringent regulation compliance is essential. Moreover, energy efficiency and safety are highly correlated in SOFC technologies and should be considered at the same level. The need for a systematic procedure to integrated process design and safety is highlighted by demonstrating how it applies to a hybrid SOFC system.

The flow and mixing behavior of two miscible liquids has been studied in an innovative static mixer by using CFD,with Reynolds numbers ranging from 20 to 160. The performance of the new mixer is compared with those of Kenics, SMX, and Komax static mixers. The pressure drop ratio(Z-factor), coefficient of variation(CoV), and extensional efficiency(α) features have been used to evaluate power consumption, distributive mixing, and dispersive mixing performances, respectively, in all mixers. The model is firstly validated based on experimental data measured for the pressure drop ratio and the coefficient of variation. CFD results are consistent with measured data and those obtained by available correlations in the literature. The new mixer shows a superior mixing performance compared to the other mixers.

In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic assumptions have been proposed as the basis for developing methodologies to predict both the mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and vessel filling degree at failure have been considered as input parameters (plus vessel volume), and the BLEVE blast energy has been estimated as output data by the ANN model. A Bayesian Regularization algorithm was chosen as the three-layer backpropagation training algorithm. Based on the neurons optimization process, the number of neurons at the hidden layer was five in the case of propane and four in the case of butane. The transfer function applied in this layer was a sigmoid, because it had an easy and straightforward differentiation for using in the backpropagation algorithm. For the output layer, the number of neurons had to be one in both cases, and the transfer function was purelin (linear). The model performance has been compared with experimental values, proving that the mechanical energy of a BLEVE explosion can be adequately predicted with the Artificial Neural Network approach.

In this work, to investigate the source of pressure fluctuations, behavior of a single bubble in a two-dimensional gas-solid fluidized bed was studied. Pressure sensors located at different heights of the bed measured presure fluctuations, and simultaneously a high speed camera was used to pursue all steps from formation to eruption of bubbles. Two types of particles were applied with different sizes and densities. Experiments showed that the maximum amplitude of formation was independent of the bubble diameter. But, it depended on density of particles, velocity of injection and the distance from bed surface. When injection stopped, there was a minimum in pressure profile related to the higher dense phase voidage for a higher superficial gas velocity after injection. Also, the maximum pressure fluctuation of bubble eruptions was related to the bubble diameter, density and size of particles. It was concluded that pressure fluctuations of formation, passing and eruption of bubbles in fluidized beds are originated due to changes in dense phase voidage, bed voidage and movement of particles during bubble eruption. (c) 2020 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

The Atmospheric Lifetime (ALT) of a compound represents the potential for the atmospheric accumulation of chemicals. Chemicals with a long lifetime are more resistant to natural decomposition and remain in the environment for a longer period. Minimum Ignition Energy (MIE) is one of the most important properties when evaluating hazardous chemicals. Despite the significance of these environment and safety-related properties, currently, there are no group contribution (GC) models that enable their predictive modeling. The present research aims at filling this gap. To this end, experimental data were collected from literature and the GC model parameters were estimated using the weighted nonlinear least-squares regression. Two approaches were applied; in the step-wise approach the parameters of the first, second and third order GC models were estimated sequentially. By comparison, all these parameters were optimized simultaneously in the second approach. The estimated average relative error and correlation coefficient for ALT model were 45.28% and 0.9999 for the step-wise approach, and 26.16% and 0.9999 for the simultaneous approach, respectively. In the case of MIE, the average relative error and correlation coefficient were 16.70% and 0.9964 for the step-wise approach and 11.48% and 0.9999 for the simultaneous approach, respectively. The proposed models not only provide novel tools for the environmental/safety analysis of the common chemicals when experimental values are unavailable, but they could also be applied to computer-aided product design problems; thus contributing towards the development of improved and more sustainable industrial processes.

Liquefaction and then transportation to the market is one of the promising options for the utilization of associated natural gas resources which are produced in oil fields. However, the flow of such resources is normally unsteady. Additionally, the associated gas in one oil field may exhaust in a few years and the liquefaction plant should be moved to another oil field with different specifications. In order to tackle such challenges, liquefaction systems not only must be optimally designed and operated but also should be flexible with respect to the gas flow fluctuations. The flexibility analysis of such processes is usually ignored in the optimization studies. In this research, first, the economic performance of two small-scale liquefaction processes (a single mixed-refrigerant process, SMR, and a nitrogen expander process) was optimized and compared. The results showed that the SMR process is economically more attractive (49% lower lifecycle cost compared to the nitrogen expander process). As a post-optimization step, flexibility analysis was performed to investigate the ability of optimal designs in overcoming gas flow fluctuations. For this purpose, five-thousand feed samples with different flowrate and methane content were supposed which formed a feasibility-check region. The results showed that with respect to the design constraints, the optimal SMR process is more flexible and feasibly operates in the entire region. However, the nitrogen expander process cannot feasibly operate for the gas feed with high flowrate and low methane content.

The present study is concerned with the computational fluid dynamics (CFD) simulation of turbulent dispersion of immiscible liquids, namely, water-silicone oil and water-benzene through Kenics static mixers using the Eulerian-Eulerian and Eulerian-Lagrangian approaches of the ANSYS Fluent 16.0 software. To study the droplet size distribution (DSD), the Eulerian formulation incorporating a population balance model (PBM) was employed. For the Eulerian-Lagrangian approach, a discrete phase model (DPM) in conjunction with the Eulerian approach for continuous phase simulation was used to predict the residence time distribution (RTD) of droplets. In both approaches, a shear stress transport (SST) k - omega turbulence model was used. For validation purposes, the simulated results were compared with the experimental data and theoretical values for the Fanning friction factor, Sauter mean diameter and the mean residence time. The reliability of the computational model was further assessed by comparing the results with the available empirical correlations for Fanning friction factor and Sauter mean diameter. In addition, the influence of important geometrical and operational parameters, including the number of mixing elements and Weber number, was studied. It was found that the proposed models are capable of predicting the performance of the Kenics static mixer reasonably well. (C) 2019 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.

In performing an efficient emergency response plan, one of the key elements is having a properly designed and well-equipped Emergency Operations Center (EOC). Lack of EOC functionality may even lead to the failure of the whole crisis management plan (CMP). Despite the significance of industrial EOC, there is not any guideline or well-established recommended practice in design and equipping these centers. In this paper, a comprehensive checklist giving recommendations on design and equipping EOCs is presented. Necessities in different fields such as configuration and layout, communication facilities, stationery and office supplies, welfare and eventually procedures are listed. Also, an evaluation method based on weighted scoring technique is offered to examine the functionality of EOC and compare the different design options.

After a short update of the current more accepted definition of BLEVE, the special features of water BLEVEs are analyzed. The stronger overpressure wave generated in the case of water as compared to that of other substances is justified in terms of volume change. Through a comparison with liquefied pressurized propane, three possibilities are analyzed: the simultaneous contribution of both the liquid and the preexisting vapor, the contribution of the liquid flash vaporization, and the contribution of the pre-existing vapor. Also a historical survey on a set of 202 BLEVE accidents –the largest sample of BLEVE accidents surveyed until now– is presented. LPG was the most common substances in this set of accidents. However, water and LNG (11% of water and 4% of LNG in the studied cases) have also been involved. Impact failure (44.8%) and human factor (30.3%) were the most common causes of BLEVEs. Transport, storage, process plants, and transfer were the activities in which more accidents occurred.

Although the main objective of utilizing gas detection systems is risk reduction by detecting high risk scenarios, majority of published placement procedures do not address risk concept quantitatively. To include this concept, a risk based methodology is proposed which consists of four key steps: Input Data, Dispersion Analysis, Risk Analysis and Optimization. In the first step, a set of release scenarios are defined and required data including frequency of release, wind rose and grid set are provided. In dispersion analysis step, the set of scenarios are simulated using a dispersion simulation tool and the ability of grid points to detect any scenario is stored in a binary matrix called detection matrix. In risk analysis, the risk of each scenario is calculated incorporating these factors: frequency, damage to personnel, asset loss and probability of delayed ignition. Once the detection matrix and risk of scenarios are provided, at last step optimal placement is performed using a risk-based objective function which is defined as the sum of the risk of undetected scenarios. The optimization formulation called MRR (Maximum Risk Reduction) is solved by a greedy approach called Dynamic Programming in which the location of detectors are determined via an iterative procedure: each time finding the grid point that can cover maximum undetected risk. The applicability of the methodology is shown in a case study and the results are compared with a coverage based formulation.

The strategies of Inherently Safer Design (ISD) provide a conceptual approach in order to design equipment and processes with substantially improved safety level. However, this may lead to a less economically attractive design. This study aimed to obtain optimal decision parameters of a reactor network system to produce allyl chloride. The objective functions were the risk level, including the severity and the frequency of the accidents, which were associated with the hazards in the network and the economic profit of the process. Based on this optimization approach, an array of optimal solutions (called Pareto front) was obtained as a trade-off between the objectives under investigation. A final design point was ultimately selected using Shannon's entropy and Bellman-Zadeh's techniques of decision making in a fuzzy environment. Results showed that the optimum reactor network leads to a highly complex system and more process control difficulties. This result was inconsistent with the simplification strategy of Inherently Safer design. In order to deal with this problem, a sensitivity analysis was performed that yielded a decision guide to decide about the desirable level of the risk as well as the optimum design.