Numerical and experimental characterization of the hydrodynamics and drying kinetics of a barbotine slurry spray

Spray drying is a basic unit operation in several process industries such as food, pharmaceutical, ceramic, and others. In this work, a Eulerian-Lagrangian three-phase simulation is presented to study the drying process of barbotine slurry droplets for the production of ceramic tiles. To this end, the simulated velocity field produced by a spray nozzle located at the Institute of Ceramic Technology in Castelló (Spain) is benchmarked against measurements obtained by means of laser Doppler anemometry in order to validate the numerical model. Also, the droplet size distribution generated by the nozzle is obtained at operating conditions by means of laser diffraction and the data obtained are compared qualitatively to those found in the literature. The characteristic Rosin-Rammler droplet size from the distribution is introduced thereafter in the three-phase simulation to analyse the drying kinetics of individual droplets. The model predicts the theoretical linear evolution of the square diameter (D2-law), and the temperature and mass exchange with the environment. The proposed model is intended to support the design and optimization of industrial spray dryers

Synthetic natural gas production from CO2 over Ni-x/CeO2-ZrO2 (x = Fe, Co) catalysts: Influence of promoters and space velocity

Herein, the production of synthetic natural gas is proposed as an effective route for CO2 conversion. Typical catalysts for this reaction are based on Ni. In this study, we demonstrated that the addition of promoters such as iron and cobalt can greatly benefit the activity of standard Ni methanation catalysts. In particular cobalt seems to be a very efficient promoter. Our Co doped material is an outstanding catalysts for the CO2 methanation leading to high levels of CO2 conversion with selectivities close to 100%. Additionally, this catalyst is able to preserve excellent performance at relatively high space velocity which allows flexibility in the reactor design making easier the development of compact CO2 utilisation units. As an additional advantage, the Co-promoted catalysts is exceptionally stable conserving high levels of CO2 conversion under continuous operations in long terms runs.Financial support for this work has been obtained from the EPSRC grants no EP/J020184/2 and EP/R512904/1. L. Pastor-Perez acknowledges Generalitat Valenciana for her postdoctoral grant (APOSTD/2017)

Assessment of the contribution of TEX air pollutants from Nigeria’s petroleum refineries to the ambient air quality: Part II

Environmental hazards associated with release of emission from petroleum refineries have caused serious concerns for the host communities. The study focused on the assessment of the contribution of Nigerian refineries to the ambient air quality. Total emission of toluene, ethyl benzene and xylene (TEX) from Nigerian twenty-six (four existing and twenty-two proposed) refineries were estimated using emission factor approach, under no-control measure (worst case scenario) option. Results showed that the four existing refineries emit 2.90 × 1013 tons of toluene, 1.93 × 1013 tons ethyl benzene and 1.06 × 1013 tons per year while the twenty-two proposed refineries have the capacity of releasing annually 9.17 × 1013 tons of toluene, 6.69 × 1013 tons of ethyl benzene and .95 x 1013 tons of xylene. If operated at full capacity, the total estimated TEX emission from the existing refineries stood at 5.89 × 1013 tons/year while the proposed refineries have the potential of adding additional 2.01 × 1014 tons/year. These concentrations were considered as environmental menace that could have adverse health challenge of the residents of the host communities. Some technologically driven measures to control and subsequently reduce TEX emission from these refineries were suggested

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Optimierung von Prozesssystemen unter Unsicherheiten mit Hilfe von Wahrscheinlichkeitsrestriktionen

In der chemischen Industrie steigt der Bedarf an flexibleren und wirksameren Produktionsanlagen, um den steigenden Anforderungen bezüglich der schnell wechselnden Marktanforderungen und der Umweltverträglichkeit gerecht zu werden. Die Erfahrung hat gezeigt, dass die in der Vergangenheit schwerpunktmäßig durchgeführte deterministische Analyse und Optimierung dieser Prozesse nicht ausreichend sind. Sie führen entweder zu einer konservativen oder einer aggressiven Prozessstrategie und oftmals zur Verletzung von entscheidenden Restriktionen wie Umweltvorgaben, Sicherheitsrestriktionen, Produkt-Spezifikationen. Hieraus resultiert, dass eine Berücksichtigung von Unsicherheiten in die dynamische Optimierung des Prozesses zur Anpassung der sich ändernden stochastischen Randbedingungen notwendig ist. Entsprechende Lösungsansätze für komplexe dynamische Prozesse fehlten. Hierfür wurde in dieser Arbeit ein systematischer Lösungsansatz zur nichtlinearen dynamischen stochastischen Optimierung größer Systeme unter sich zeitlich ändernden Randbedingungen entwickelt und erfolgreich an verschiedene Beispielprozesse angewandt. Für die Erprobung der Methodik wurden rigorose Prozessmodelle einbezogen. Es galt insbesondere, die Einflüsse der unsicheren Größen auf die nichtmessbaren Variablen zu berücksichtigen. Diese Variablen entsprechen häufig relevanten Größen wie z.B. Produktqualitäten. Zu diesem Zweck wurden Optimierungsprobleme unter Wahrscheinlichkeitsrestriktionen formuliert und an die konkrete Fragestellung angepasste Lösungswege entwickelt. Der entwickelte Ansatz stellt in jedem Fall eine signifikante Erweiterung der Stochastischen Programmierung dar. Die hieraus abgeleitete Methodik beschreibt eine allgemeine Vorgehensweise zur optimalen und robusten Prozessführung unter Unsicherheiten.In this thesis, new approaches for chance constrained programming of large scale nonlinear dynamic systems under time-dependent uncertainty are introduced. The stochastic nature of the uncertainties is explicitly considered in the problem formulation in which some input and state constraints are to be complied with predefined probability levels. The developed methods consider a nonlinear relation between the uncertain input and the constrained variables. Efficient algorithms are applied to compute the probabilities and, simultaneously, the gradients through integration by collocation in finite elements. The formulation of single or joint probability limits incorporates the consideration of feasibility and that of the trade-off between robustness and profitability regarding the objective function values. The new approaches are relevant to all cases when uncertainty can be described by any kind of joint correlated multivariate distribution function. The potential and the efficiency of the presented systematic methodology are illustrated with application to different processes under uncertainty, in particular, transient processes. Moreover, the functionality and efficiency of the developed chance constrained framework are demonstrated throughout on examples of design, operation and control problems. Furthermore, two model based approaches are developed to provide a close integration of dynamic real time optimization and control and to cope with uncertainty

A Mathematical Programming Approach to Optimal Design of Smart Distributed Energy Systems

The UK is committed to reducing its greenhouse gas emissions by at least 80% by 2050, relative to 1990 levels. For this to happen, we need to transform the UK economy while ensuring secure, low-carbon energy supplies to 2050. The future electricity distribution system, known as smart grid, will integrate advanced digital meters, distribution automation, communication systems and distributed energy resources. There has been a lot of discussion about the importance of the Internet of Things (IoT) in future smart grids and smart cities stating that IoT offers many applications and can be used to integrate efficiency renewable energy sources in the smart grid by making the electricity grid more robust and scalable. This study will focus on the development of an integrated IoT-Distributed energy systems (DES) model for the efficient energy management of a microgrid under the integration of the intermittent renewable energy resources. In this work, we expand the definition of flexible options to include demand and supply together with design and operation strategies using internet of things (IoT). Our framework brings weather data and sensor information into a virtual energy plant optimisation model that connects supplier and consumer to optimise potential flexibility gaps arising from supply and demand mismatch. The problem is posed as a hybrid mixed-integer linear programming (MILP) optimisation model combining flexibility analysis and optimal synthesis for integrating energy supply and demand, where environmental information is added to each stage. Finally, we combine traditional mathematical programming approaches such as flexibility analysis and optimal network synthesis and within a single optimisation framework combining IoT and urban DES

Solar Hydrogen Production via Aqueous Methanol Electrolysis

Despite having very clean combustion properties, the majority of hydrogen produced today still comes from fossil fuels. As such, there is a demand for renewably produced hydrogen, such as solar powered electrolysis, so that the hydrogen produced retains its clean credentials. Unfortunately, this process is plagued by inefficiencies and requires improvement in order to economically compete with fossil fuels. This work investigates solar hydrogen production via aqueous methanol (MeOH) electrolysis in comparison to pure water electrolysis in a directly coupled solar-PEM electrolysis system. Experiments were completed to investigate the impact of changing the MeOH concentration, power supply, and load characteristics on electrolysis and solar-hydrogen efficiencies. Simulation studies were then performed to analyse thoroughly the experimental data so as to gain an understanding of the yields and economics of utility scale solar–hydrogen facilities

Model Based Analysis of a Petroleum Refinery Plant with Hydrotreating as a Pre-treatment Unit

Catalytic hydrotreating is one of the processes used intensively in the modern petroleum refining industry. It is series of reactions considered as a mature process that improves the quality of petroleum products and removes Sulphur and undesired impurities. This study aims to develop and enhance the performance of a whole petroleum refining plant, which follows the concept of crude oil hydrotreating (HDT). The study was carried out using Aspen HYSYS simulator building a model-based analysis for the refinery plant. Two refineries have been simulated separately; one with a crude oil hydrotreating and the other followed the conventional method. The comparison and analysis focused on enhancing the yield of middle distillates while reducing the total energy consumption and overall costs. Hydrodenitrogenation and Hydrodesulfurization were the two reactions that took place in the trickle bed reactor at 400 °C and 10 MPa. The hydrotreated crude oil enters then the atmospheric distillation column, where six main products were distilled (LPG, Light Naphtha, Heavy Naphtha, Kerosene and Residual crude). In the model-based analysis, the crude HDT process configuration was completed first using Kirkuk crude oil, and to confirm the significance of the study, Siberian crude was used as an alternative feedstock. Finally, the results confirmed that the crude oil hydrotreating method can be followed using different types of feedstock around the world

Modelling of the Thermal Performance of SGSP using COMSOL Multiphysics

Novel renewable energy sources are necessary to counter the current environmental crisis. The largest source of renewable energy is the sun. One possible application of solar energy is the harvesting and storage of low temperature thermal heat (< 100°C). A very promising technology that can harvest and store thermal energy is a solar pond. To assess the thermal performance of a solar pond, more accurate and reliable theoretical models need to be developed. The preponderance of models use empirical relationships with little justification. This work examines an existing 1D theoretical model and develops and validates a novel theoretical model in COMSOL Multiphysics in both 2- and 3-D. The new models were compared with experimental data from two different test sites, concerning mainly the temperature at the lower convective zone (LCZ) and the upper convective zone (UCZ). The 3D model was proven to be the most accurate with the 1D model being the least. Furthermore, the general radiative heat transfer equation, with an isotropic scattering phase function, solved using the discrete ordinates method was proven to give a satisfactory accuracy in terms of radiation in semi-transparent media