This paper describes the combinational surface kinetics enhancement and surface states passivation of nickel-borate (Ni-Bi) co-catalyst for a hematite (Fe2O3) photoanode. The Ni-Bi-modified Fe2O3 photoanode exhibits a cathodic onset potential shift of 230 mV and a 2.3-fold enhancement of the photocurrent at 1.23 V, versus the reversible hydrogen electrode (RHE). The borate (Bi) in the Ni-Bi film promotes the release of protons for the oxygen evolution reaction (OER).
Green process engineering, which is based on the principles of the process intensiﬁcation strategy, can provide an important contribution toward achieving industrial sustainable development. Green process engineering refers to innovative equipment and process methods that are expected to bring about substantial improvements in chemical and any other manufacturing and processing aspects. It includes decreasing production costs, equipment size, energy consumption, and waste generation, and improving remote control, information ﬂuxes, and process ﬂexibility. Membrane-based technology assists in the pursuit of these principles, and the potential of membrane operations has been widely recognized in the last few years. This work starts by presenting an overview of the membrane operations that are utilized in water treatment and in the production of energy and raw materials. Next, it describes the potential advantages of innovative membrane-based integrated systems. A case study on an integrated membrane system (IMS) for seawater desalination coupled with raw materials production is presented. The aim of this work is to show how membrane systems can contribute to the realization of the goals of zero liquid discharge (ZLD), total raw materials utilization, and low energy consumption.
The current irrational use of fossil fuels and the impact of greenhouse gases on the environment are driving research into renewable energy production from organic resources and waste. The global energy demand is high, and most of this energy is produced from fossil resources. Recent studies report that anaerobic digestion (AD) is an efficient alternative technology that combines biofuel production with sustainable waste management, and various technological trends exist in the biogas industry that enhance the production and quality of biogas. Further investments in AD are expected to meet with increasing success due to the low cost of available feedstocks and the wide range of uses for biogas (i.e., for heating, electricity, and fuel). Biogas production is growing in the European energy market and offers an economical alternative for bioenergy production. The objective of this work is to provide an overview of biogas production from lignocellulosic waste, thus providing information toward crucial issues in the biogas economy.
Cadaverine, a natural polyamine with multiple bioactivities that is widely distributed in prokaryotes and eukaryotes, is becoming an important industrial chemical. Cadaverine exhibits broad prospects for various applications, especially as an important monomer for bio-based polyamides. Cadaverine-based polyamide PA 5X has broad application prospects owing to its environmentally friendly characteristics and exceptional performance in water absorption and dimensional stability. In this review, we summarize recent findings on the biosynthesis, metabolism, and physiological function of cadaverine in bacteria, with a focus on the regulatory mechanism of cadaverine synthesis in Escherichia coli (E. coli). We also describe recent developments in bacterial production of cadaverine by direct fermentation and whole-cell bioconversion, and recent approaches for the separation and purification of cadaverine. In addition, we present an overview of the application of cadaverine in the synthesis of completely bio-based polyamides. Finally, we provide an outlook and suggest future developments to advance the production of cadaverine from renewable resources.
Photosynthetic microorganisms are important bioresources for producing desirable and environmentally benign products, and photobioreactors (PBRs) play important roles in these processes. Designing PBRs for photocatalysis is still challenging at present, and most reactors are designed and scaled up using semi-empirical approaches. No appropriate types of PBRs are available for mass cultivation due to the reactors’ high capital and operating costs and short lifespan, which are mainly due to a current lack of deep understanding of the coupling of light, hydrodynamics, mass transfer, and cell growth in efficient reactor design. This review provides a critical overview of the key parameters that influence the performance of the PBRs, including light, mixing, mass transfer, temperature, pH, and capital and operating costs. The lifespan and the costs of cleaning and temperature control are also emphasized for commercial exploitation. Four types of PBRs—tubular, plastic bag, column airlift, and flat-panel airlift reactors are recommended for large-scale operations. In addition, this paper elaborates the modeling of PBRs using the tools of computational fluid dynamics for rational design. It also analyzes the difficulties in the numerical simulation, and presents the prospect for mechanism-based models.
Wastewater treatment is a process that is vital to protecting both the environment and human health. At present, the most cost-effective way of treating wastewater is with biological treatment processes such as the activated sludge process, despite their long operating times. However, population increases have created a demand for more efficient means of wastewater treatment. Fluidization has been demonstrated to increase the efficiency of many processes in chemical and biochemical engineering, but it has not been widely used in large-scale wastewater treatment. At the University of Western Ontario, the circulating fluidized-bed bioreactor (CFBBR) was developed for treating wastewater. In this process, carrier particles develop a biofilm composed of bacteria and other microbes. The excellent mixing and mass transfer characteristics inherent to fluidization make this process very effective at treating both municipal and industrial wastewater. Studies of lab- and pilot-scale systems showed that the CFBBR can remove over 90% of the influent organic matter and 80% of the nitrogen, and produces less than one-third as much biological sludge as the activated sludge process. Due to its high efficiency, the CFBBR can also be used to treat wastewaters with high organic solid concentrations, which are more difficult to treat with conventional methods because they require longer residence times; the CFBBR can also be used to reduce the system size and footprint. In addition, it is much better at handling and recovering from dynamic loadings (i.e., varying influent volume and concentrations) than current systems. Overall, the CFBBR has been shown to be a very effective means of treating wastewater, and to be capable of treating larger volumes of wastewater using a smaller reactor volume and a shorter residence time. In addition, its compact design holds potential for more geographically localized and isolated wastewater treatment systems.
Crystallization is one of the oldest separation and purification unit operations, and has recently contributed to significant improvements in producing higher-value products with specific properties and in building efficient manufacturing processes. In this paper, we review recent developments in crystal engineering and crystallization process design and control in the pharmaceutical industry. We systematically summarize recent methods for understanding and developing new types of crystals such as co-crystals, polymorphs, and solvates, and include several milestones such as the launch of the first co-crystal drug, Entresto (Novartis), and the continuous manufacture of Orkambi (Vertex). Conventional batch and continuous processes, which are becoming increasingly mature, are being coupled with various control strategies and the recently developed crystallizers are thus adapting to the needs of the pharmaceutical industry. The development of crystallization process design and control has led to the appearance of several new and innovative crystallizer geometries for continuous operation and improved performance. This paper also reviews major recent progress in the area of process analytical technology.
Crystallization is an important unit operation in the pharmaceutical industry. At present, most pharmaceutical crystallization processes are performed in batches. However, due to product variability from batch to batch and to the low productivity of batch crystallization, continuous crystallization is gaining increasing attention. In the past few years, progress has been made to allow the products of continuous crystallization to meet different requirements. This review summarizes the progress in pharmaceutical continuous crystallization from a product engineering perspective. The advantages and disadvantages of different types of continuous crystallization are compared, with the main difference between the two main types of crystallizers being their difference in residence time distribution. Approaches that use continuous crystallization to meet different quality requirements are summarized. Continuous crystallization has advantages in terms of size and morphology control. However, it also has the problem of a process yield that may be lower than that of a batch process, especially in the production of chirality crystals. Finally, different control strategies are compared.
Photocatalytic water splitting, which directly converts solar energy into hydrogen, is one of the most desirable solar-energy-conversion approaches. The ultimate target of photocatalysis is to explore efficient and stable photocatalysts for solar water splitting. Tantalum (oxy)nitride-based materials are a class of the most promising photocatalysts for solar water splitting because of their narrow bandgaps and sufficient band energy potentials for water splitting. Tantalum (oxy)nitride-based photocatalysts have experienced intensive exploration, and encouraging progress has been achieved over the past years. However, the solar-to-hydrogen (STH) conversion efficiency is still very far from its theoretical value. The question of how to better design these materials in order to further improve their water-splitting capability is of interest and importance. This review summarizes the development of tantalum (oxy)nitride-based photocatalysts for solar water spitting. Special interest is paid to important strategies for improving photocatalytic water-splitting efficiency. This paper also proposes future trends to explore in the research area of tantalum-based narrow bandgap photocatalysts for solar water splitting.
After two decades’ endeavor, the Research Institute of Petroleum Processing (RIPP) has successfully developed a green caprolactam (CPL) production technology. This technology is based on the integration of titanium silicate (TS)-1 zeolite with the slurry-bed reactor for the ammoximation of cyclohexanone, the integration of silicalite-1 zeolite with the moving-bed reactor for the gas-phase rearrangement of cyclohexanone oxime, and the integration of an amorphous nickel (Ni) catalyst with the magnetically stabilized bed reactor for the purification of caprolactam. The world’s first industrial plant based on this green CPL production technology has been built and possesses a capacity of 200?kt·a−1. Compared with existing technologies, the plant investment is pronouncedly reduced, and the nitrogen (N) atom utilization is drastically improved. The waste emission is reduced significantly; for example, no ammonium sulfate byproduct is produced. As a result, the price difference between CPL and benzene drops. In 2015, the capacity of the green CPL production technology reached 3?×?106?t·a−1, making China the world’s largest CPL producer, with a global market share exceeding 50%.
Nitrogen-doped carbon nanotubes (NCNTs) were used as a support for iron (Fe) nanoparticles applied in carbon dioxide (CO2) hydrogenation at 633 K and 25 bar (1 bar= 105 Pa). The Fe/NCNT catalyst promoted with both potassium (K) and manganese (Mn) showed high performance in CO2 hydrogenation, reaching 34.9% conversion with a gas hourly space velocity (GHSV) of 3.1 L·(g·h)−1. Product selectivities were high for olefin products and low for short-chain alkanes for the K-promoted catalysts. When Fe/NCNT catalyst was promoted with both K and Mn, the catalytic activity was stable for 60 h of reaction time. The structural effect of the Mn promoter was demonstrated by X-ray diffraction (XRD), temperature-programmed reduction (TPR) with molecular hydrogen (H2), and in situ X-ray absorption near-edge structure (XANES) analysis. The Mn promoter stabilized wüstite (FeO) as an intermediate and lowered the TPR onset temperature. Catalytic ammonia (NH3) decomposition was used as an additional probe reaction for characterizing the promoter effects. The Fe/NCNT catalyst promoted with both K and Mn had the highest catalytic activity, and the Mn-promoted Fe/NCNT catalysts had the highest thermal stability under reducing conditions.
Solar-powered carbon dioxide (CO2)-to-fuel conversion presents itself as an ideal solution for both CO2 mitigation and the rapidly growing world energy demand. In this work, the heating effect of light irradiation onto a bed of supported nickel (Ni) catalyst was utilized to facilitate CO2 conversion. Ceria (CeO2)-titania (TiO2) oxide supports of different compositions were employed and their effects on photothermal CO2 conversion examined. Two factors are shown to be crucial for instigating photothermal CO2 methanation activity: ① Fine nickel deposits are required for both higher active catalyst area and greater light absorption capacity for the initial heating of the catalyst bed; and ② the presence of defect sites on the support are necessary to promote adsorption of CO2 for its subsequent activation. Titania inclusion within the support plays a crucial role in maintaining the oxygen vacancy defect sites on the (titanium-doped) cerium oxide. The combination of elevated light absorption and stabilized reduced states for CO2 adsorption subsequently invokes effective photothermal CO2 methanation when the ceria and titania are blended in the ideal ratio(s).
The synthesis of fluorescent nanomaterials has received considerable attention due to the great potential of these materials for a wide range of applications, from chemical sensing through bioimaging to optoelectronics. Herein, we report a facile and scalable approach to prepare fluorescent carbon dots (FCDs) via a one-pot reaction of citric acid with ethylenediamine at 150 °C under ambient air pressure. The resultant FCDs possess an optical bandgap of 3.4 eV and exhibit strong excitation-wavelength-independent blue emission (λEm= 450 nm) under either one- or two-photon excitation. Owing to their low cytotoxicity and long fluorescence lifetime, these FCDs were successfully used as internalized fluorescent probes in human cancer cell lines (HeLa cells) for two-photon excited imaging of cells by fluorescence lifetime imaging microscopy with high-contrast resolution. They were also homogenously mixed with commercial inks and used to draw fluorescent patterns on normal papers and on many other substrates (e.g., certain flexible plastic films, textiles, and clothes). Thus, these nanomaterials are promising for use in solid-state fluorescent sensing, security labeling, and wearable optoelectronics.
Natural adsorbents such as banana pseudostem can play a vital role in the removal of heavy metal elements from wastewater. Major water resources and chemical industries have been encountering difficulties in removing heavy metal elements using available conventional methods. This work demonstrates the potential to treat various effluents utilizing natural materials. A characterization of banana pseudostem powder was performed using environmental scanning electron microscopy (ESEM) and Fourier-transform infrared (FTIR) spectroscopy before and after the adsorption of lead(II). Experiments were carried out using a batch process for the removal of lead(II) from an aqueous solution. The effects of the adsorption kinetics were studied by altering various parameters such as initial pH, adsorbent dosage, initial lead ion concentration, and contact time. The results show that the point of zero charge (PZC) for the banana pseudostem powder was achieved at a pH of 5.5. The experimental data were analyzed using isotherm and kinetic models. The adsorption of lead(II) onto banana pseudostem powder was fitted using the Langmuir adsorption isotherm. The adsorption capacity was found to be 34.21 mg·g−1, and the pseudo second-order kinetic model showed the best fit. The optimum conditions were found using response surface methodology. The maximum removal was found to be 89%.
This work aims to understand the gasification performance of municipal solid waste (MSW) by means of thermodynamic analysis. Thermodynamic analysis is based on the assumption that the gasification reactions take place at the thermodynamic equilibrium condition, without regard to the reactor and process characteristics. First, model components of MSW including food, green wastes, paper, textiles, rubber, chlorine-free plastic, and polyvinyl chloride were chosen as the feedstock of a steam gasification process, with the steam temperature ranging from 973 K to 2273 K and the steam-to-MSW ratio (STMR) ranging from 1 to 5. It was found that the effect of the STMR on the gasification performance was almost the same as that of the steam temperature. All the differences among the seven types of MSW were caused by the variation of their compositions. Next, the gasification of actual MSW was analyzed using this thermodynamic equilibrium model. It was possible to count the inorganic components of actual MSW as silicon dioxide or aluminum oxide for the purpose of simplification, due to the fact that the inorganic components mainly affected the reactor temperature. A detailed comparison was made of the composition of the gaseous products obtained using steam, hydrogen, and air gasifying agents to provide basic knowledge regarding the appropriate choice of gasifying agent in MSW treatment upon demand.
The present work explores the application of microwave heating for the melting of powdered tin. The morphology and particle size of powdered tin prepared by the centrifugal atomization method were characterized. The tin particles were uniform and spherical in shape, with 90% of the particles in the size range of 38–75 μm. The microwave absorption characteristic of the tin powder was assessed by an estimation of the dielectric properties. Microwave penetration was found to have good volumetric heating on powdered tin. Conduction losses were the main loss mechanisms for powdered tin by microwave heating at temperatures above 150 °C. A 20 kW commercial-scale microwave tin-melting unit was designed, developed, and utilized for production. This unit achieved a heating rate that was at least 10 times higher than those of conventional methods, as well as a far shorter melting duration. The results suggest that microwave heating accelerates the heating rate and shortens the melting time. Tin recovery rate was 97.79%, with a slag ratio of only 1.65% and other losses accounting for less than 0.56%. The unit energy consumption was only 0.17 (kW·h)·kg−1—far lower than the energy required by conventional melting methods. Thus, the microwave melting process improved heating efficiency and reduced energy consumption.
Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.
The challenges posed by smart manufacturing for the process industries and for process systems engineering (PSE) researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, but benchmarking would give greater confidence. Technical challenges confronting process systems engineers in developing enabling tools and techniques are discussed regarding flexibility and uncertainty, responsiveness and agility, robustness and security, the prediction of mixture properties and function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to drive agility will require tackling new challenges, such as how to ensure the consistency and confidentiality of data through long and complex supply chains. Modeling challenges also exist, and involve ensuring that all key aspects are properly modeled, particularly where health, safety, and environmental concerns require accurate predictions of small but critical amounts at specific locations. Environmental concerns will require us to keep a closer track on all molecular species so that they are optimally used to create sustainable solutions. Disruptive business models may result, particularly from new personalized products, but that is difficult to predict.
This work uses a mathematical optimization approach to analyze and compare facilities that either capture carbon dioxide (CO2) artificially or use naturally captured CO2 in the form of lignocellulosic biomass toward the production of the same product, dimethyl ether (DME). In nature, plants capture CO2 via photosynthesis in order to grow. The design of the first process discussed here is based on a superstructure optimization approach in order to select technologies that transform lignocellulosic biomass into DME. Biomass is gasified; next, the raw syngas must be purified using reforming, scrubbing, and carbon capture technologies before it can be used to directly produce DME. Alternatively, CO2 can be captured and used to produce DME via hydrogenation. Hydrogen (H2) is produced by splitting water using solar energy. Facilities based on both photovoltaic (PV) solar or concentrated solar power (CSP) technologies have been designed; their monthly operation, which is based on solar availability, is determined using a multi-period approach. The current level of technological development gives biomass an advantage as a carbon capture technology, since both water consumption and economic parameters are in its favor. However, due to the area required for growing biomass and the total amount of water consumed (if plant growing is also accounted for), the decision to use biomass is not a straightforward one.
Most olefins (e.g., ethylene and propylene) will continue to be produced through steam cracking (SC) of hydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investing very little in alternative technologies and feedstocks because of their current lack of economic viability, despite decreasing crude oil reserves and the recognition of global warming. In this perspective, some of the most promising alternatives are compared with the conventional SC process, and the major bottlenecks of each of the competing processes are highlighted. These technologies emerge especially from the abundance of cheap propane, ethane, and methane from shale gas and stranded gas. From an economic point of view, methane is an interesting starting material, if chemicals can be produced from it. The huge availability of crude oil and the expected substantial decline in the demand for fuels imply that the future for proven technologies such as Fischer-Tropsch synthesis (FTS) or methanol to gasoline is not bright. The abundance of cheap ethane and the large availability of crude oil, on the other hand, have caused the SC industry to shift to these two extremes, making room for the on-purpose production of light olefins, such as by the catalytic dehydrogenation of propane.
Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-driven environment. Using real-time and high-value support systems, smart manufacturing enables a coordinated and performance-oriented manufacturing enterprise that responds quickly to customer demands and minimizes energy and material usage, while radically improving sustainability, productivity, innovation, and economic competitiveness. In this paper, several examples of the application of so-called “smart manufacturing” for the petrochemical sector are demonstrated, such as the fault detection of a catalytic cracking unit driven by big data, advanced optimization for the planning and scheduling of oil refinery sites, and more. Key scientific factors and challenges for the further smart manufacturing of chemical and petrochemical processes are identified.
In the globalized market environment, increasingly significant economic and environmental factors within complex industrial plants impose importance on the optimization of global production indices; such optimization includes improvements in production efficiency, product quality, and yield, along with reductions of energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelligence optimization methods and technologies in improving the performance of global production indices in mineral processing. First, we provide the problem description. Next, we summarize recent progress in data-based optimization for mineral processing plants. This optimization consists of four layers: optimization of the target values for monthly global production indices, optimization of the target values for daily global production indices, optimization of the target values for operational indices, and automation systems for unit processes. We briefly overview recent progress in each of the different layers. Finally, we point out opportunities for future works in data-based optimization for mineral processing plants.
The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR) and normalized multiparametric disaggregation technique (NMDT) to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.
In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solution of mixed-integer nonlinear programming (MINLP) problems are developed, with their main focus being on process synthesis problems. The algorithms are developed for the special case in which the nonlinearities arise because of logarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric case (p-MINLP). The key idea is to formulate and solve the square system of the first-order Karush-Kuhn-Tucker (KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution techniques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, two process synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicit function of the uncertain parameters.
Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis (MclustDA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression (MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search (SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in order to evaluate its performance.
This paper deals with a first-principle mathematical model that describes the electrostatic coalescer units devoted to the separation of water from oil in water-in-oil emulsions, which are typical of the upstream operations in oil fields. The main phenomena governing the behavior of the electrostatic coalescer are described, starting from fundamental laws. In addition, the gradual coalescence of the emulsion droplets is considered in the mathematical modeling in a dynamic fashion, as the phenomenon is identified as a key step in the overall yield of the unit operation. The resulting differential system with boundary conditions is then integrated via performing numerical libraries, and the simulation results confirm the available literature and the industrial data. A sensitivity analysis is provided with respect to the main parameters. The mathematical model results in a flexible tool that is useful for the purposes of design, unit behavior prediction, performance monitoring, and optimization.
Carbon capture and storage (CCS) technology will play a critical role in reducing anthropogenic carbon dioxide (CO2) emission from fossil-fired power plants and other energy-intensive processes. However, the increment of energy cost caused by equipping a carbon capture process is the main barrier to its commercial deployment. To reduce the capital and operating costs of carbon capture, great efforts have been made to achieve optimal design and operation through process modeling, simulation, and optimization. Accurate models form an essential foundation for this purpose. This paper presents a study on developing a more accurate rate-based model in Aspen Plus® for the monoethanolamine (MEA)-based carbon capture process by multistage model validations. The modeling framework for this process was established first. The steady-state process model was then developed and validated at three stages, which included a thermodynamic model, physical properties calculations, and a process model at the pilot plant scale, covering a wide range of pressures, temperatures, and CO2 loadings. The calculation correlations of liquid density and interfacial area were updated by coding Fortran subroutines in Aspen Plus®. The validation results show that the correlation combination for the thermodynamic model used in this study has higher accuracy than those of three other key publications and the model prediction of the process model has a good agreement with the pilot plant experimental data. A case study was carried out for carbon capture from a 250 MWe combined cycle gas turbine (CCGT) power plant. Shorter packing height and lower specific duty were achieved using this accurate model.
As the demand for energy continues to increase, shale gas, as an unconventional source of methane (CH4), shows great potential for commercialization. However, due to the ultra-low permeability of shale gas reservoirs, special procedures such as horizontal drilling, hydraulic fracturing, periodic well shut-in, and carbon dioxide (CO2) injection may be required in order to boost gas production, maximize economic benefits, and ensure safe and environmentally sound operation. Although intensive research is devoted to this emerging technology, many researchers have studied shale gas design and operational decisions only in isolation. In fact, these decisions are highly interactive and should be considered simultaneously. Therefore, the research question addressed in this study includes interactions between design and operational decisions. In this paper, we first establish a full-physics model for a shale gas reservoir. Next, we conduct a sensitivity analysis of important design and operational decisions such as well length, well arrangement, number of fractures, fracture distance, CO2 injection rate, and shut-in scheduling in order to gain in-depth insights into the complex behavior of shale gas networks. The results suggest that the case with the highest shale gas production may not necessarily be the most profitable design; and that drilling, fracturing, and CO2 injection have great impacts on the economic viability of this technology. In particular, due to the high costs, enhanced gas recovery (EGR) using CO2 does not appear to be commercially competitive, unless tax abatements or subsidies are available for CO2 sequestration. It was also found that the interactions between design and operational decisions are significant and that these decisions should be optimized simultaneously.
In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is applied to search for a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-based post-combustion carbon capture under different carbon dioxide (CO2) allowance market conditions. The objective of the decision maker for the power plant is to maximize the discounted cumulative profit during the power plant lifetime. Two constraints are considered for the objective formulation. Firstly, the tradeoff between the energy-intensive carbon capture and the electricity generation should be made under presumed fixed fuel consumption. Secondly, the CO2 allowances purchased from the CO2 allowance market should be approximately equal to the quantity of CO2 emission from power generation. Three case studies are demonstrated thereafter. In the first case, we show the convergence of the Sarsa TD algorithm and find a deterministic optimal bidding and operation strategy. In the second case, compared with the independently designed operation and bidding strategies discussed in most of the relevant literature, the Sarsa TD-based unified bidding and operation strategy with time-varying flexible market-oriented CO2 capture levels is demonstrated to help the power plant decision maker gain a higher discounted cumulative profit. In the third case, a competitor operating another power plant identical to the preceding plant is considered under the same CO2 allowance market. The competitor also has carbon capture facilities but applies a different strategy to earn profits. The discounted cumulative profits of the two power plants are then compared, thus exhibiting the competitiveness of the power plant that is using the unified bidding and operation strategy explored by the Sarsa TD algorithm.
This paper presents a concise summary of recent studies on the long-term variations of haze in North China and on the environmental and dynamic conditions for severe persistent haze events. Results indicate that haze days have an obviously rising trend over the past 50 years in North China. The occurrence frequency of persistent haze events has a similar rising trend due to the continuous rise of winter temperatures, decrease of surface wind speeds, and aggravation of atmospheric stability. In North China, when severe persistent haze events occur, anomalous southwesterly winds prevail in the lower troposphere, providing sufficient moisture for the formation of haze. Moreover, North China is mainly controlled by a deep downdraft in the mid-lower troposphere, which contributes to reducing the thickness of the planetary boundary layer, obviously reducing the atmospheric capacity for pollutants. This atmospheric circulation and sinking motion provide favorable conditions for the formation and maintenance of haze in North China.