A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

Real World\u27 star Danny Roberts kicks off Pride Week

Approximately 250 University of Maine students and community members got a taste of what it\u27s like to be Out in the Real World Monday night in 101 Neville Hall. As part of the University of Maine\u27s Pride Week. MTV Real World\u27s 2000 New Orleans cast member Danny Roberts spoke a little about his Real World experience and more about what it\u27s like being gay in the military

A multi-criteria design framework for the synthesis of complex pressure swing adsorption cycles for CO2 capture

Pressure Swing Adsorption (PSA) is the most efficient option for middle scale separation processes. PSA is a cyclic process whose main steps are adsorption, at high pressure, and regeneration of the adsorbent, at low pressure. The design of PSA cycles is still mainly approached experimentally due to the computational challenges posed by the complexity of the simulation and by the need to detect the performance at cyclic steady state (CSS). Automated tools for the design of PSA processes are desirable to allow a better understanding of the the complex relationship between the performance and the design variables. Furthermore, the operation is characterised by trade-o�ffs between conflicting criteria. A multi-objective flowsheet design framework for complex PSA cycles is presented. A suite of evolutionary procedures, for the generation of alternative PSA con�figurations has been developed, including simple evolution, simulated annealing as well as a population based procedure. Within this evolutionary procedure the evaluation of each cycle confi�guration generated requires the solution of a multi-objective optimisation problem which considers the conflicting objectives of recovery and purity. For this embedded optimisation problem a multi-objective genetic algorithm (MOGA), with a targeted fi�tness function, is used to generate the approximation to the Pareto front. The evaluation of each alternative design makes use of a number of techniques to reduce the computational burden. The case studies considered include the separation of air for N2 production, a fast cycle operation which requires a detailed di�ffusion model, and the separation of CO2 from flue gases, where complex cycles are needed to achieve a high purity product. The novel design framework is able to determine optimal configurations and operating conditions for PSA for these industrially relevant case studies. The results presented by the design framework can help an engineer to make informed design decisions

Direct current (DC) resistivity and Induced Polarization (IP) monitoring of active layer dynamics at high temporal resolution

With permafrost thawing and changes in active layer dynamics induced by climate change, interactions between biogeochemical and thermal processes in the ground are of great importance. Here, active layer dynamics have been monitored using direct current (DC) resistivity and induced polarization (IP) measurements at high temporal resolution and at a relatively large scale at a heath tundra site on Disko Island on the west coast of Greenland (69 degrees N). At the field site, the active layer is disconnected from the deeper permafrost, due to isothermal springs in the region. Borehole sediment characteristics and subsurface temperatures supplemented the DC-IF measurements. A time-lapse DC-IP monitoring system has been acquiring at least six datasets per day on a 42-electrode profile with 0.5 m electrode spacing since July 2013. Remote control of the data acquisition system enables interactive adaptation of the measurement schedule, which is critically important to acquire data in the winter months, where extremely high contact resistances increase the demands on the resistivity meter. Data acquired during the freezing period of October 2013 to February 2014 clearly image the soil freezing as a strong increase in resistivity. While the freezing horizon generally moves deeper with time, some variations in the freezing depth are observed along the profile. Comparison with depth-specific soil temperature indicates an exponential relationship between resistivity and below-freezing temperature. Time-lapse inversions of the full-decay IF data indicate a decrease of normalized chargeability with freezing of the ground, which is the result of a decrease in the total unfrozen water and of the higher ion concentration in the pore-water. We conclude that DC-IP time-lapse measurements can non-intrusively and reliably image freezing patterns and their lateral variation on a 10-100 m scale that is difficult to sample by point measurements. In combination with laboratory experiments, the different patterns in resistivity and chargeability changes will enable the disentanglement of processes (e.g., fluid migration and freezing, advective and diffusive heat transport) occurring during freezing of the ground. The technology can be expanded to three dimensions and also to larger scale

Influence of non-aqueous phase liquid configuration on induced polarization parameters: Conceptual models applied to a time-domain field case study

Resistivity and induced polarization (IP) measurements on soil contaminated with non-aqueous phase liquids (NAPLs) show a great variety in results in previous research. Several laboratory studies have suggested that the presence of NAPLs in soil samples generally decrease the magnitude of the IP-effect, while others have indicated the opposite. A number of conceptual models have been proposed suggesting that NAPLs can alter the pore space in different ways, e.g. by coating the grain surfaces and thus inhibiting grain polarization, or by changing the pore throat size and thus affecting the membrane polarization mechanism. The main aim of this paper is to review previously published conceptual models and to introduce some new concepts of possible residual NAPL configurations in the pore space. Time domain induced polarization measurements were performed at a NAPL contaminated field site, and the data were inverted using the Constant Phase Angle (CPA) model and the Cole–Cole model respectively. No significant phase anomalies were observed in the source area of the contamination when the CPA inverted profiles were compared with soil sampling results of free-phase contaminant concentrations. However, relatively strong phase and normalized phase anomalies appeared next to the source area, where residual free-phase presence could be expected according to the chemical data. We conclude that depending on the NAPL configuration, different spectral IP responses can be expected. In previous research, the NAPL configurations in different samples or field sites are often unknown, and this may to some extent explain why different results have been achieved by different authors. In our field case, we believe that the NAPL forms a more or less continuous phase in the pore space of the source zone leading to an absence of IP anomalies. The increase in phase and normalized phase angle observed next to the source zone is interpreted as a degradation zone. The ongoing biodegradation may have led to a fractionation of the continuous NAPL in the outer part of the original source zone, leading to residual presence of isolated NAPL droplets in the soil pores. With such NAPL configurations, an increased polarization can be expected according to the electrochemical- and membrane polarization mechanisms. More research is needed to confirm the effects of different NAPL configuration on spectral IP parameters

Re-parameterisations of the Cole-Cole model for improved spectral inversion of induced polarization data

The induced polarization phenomenon, both in time domain and frequency domain, is often parameterised using the empirical Cole-Cole model. To improve the resolution of model parameters and to decrease the parameter correlations in the inversion process of induced polarization data, we suggest here three re-parameterisations of the Cole-Cole model, namely the maximum phase angle Cole-Cole model, the maximum imaginary conductivity Cole-Cole model, and the minimum imaginary resistivity Cole-Cole model. The maximum phase angle Cole-Cole model uses the maximum phase \u3c6max and the inverse of the phase peak frequency, \u3c4\u3c6, instead of the intrinsic charge-ability m0 and the time constant adopted in the classic Cole-Cole model. The maximum imaginary conductivity Cole-Cole model uses the maximum imaginary conductivity \u3c3max\u2033 instead of m0 and the time constant \u3c4\u3c3 of the Cole-Cole model in its conductivity form. The minimum imaginary resistivity Cole-Cole model uses the minimum imaginary resistivity \u3c1min\u2033 instead of m0 and the time constant \u3c4\u3c1 of the Cole-Cole model in its resistivity form. The effects of the three re-parameterisations have been tested on synthetic timedomain and frequency-domain data using a Markov chain Monte Carlo inversion method, which allows for easy quantification of parameter uncertainty, and on field data using 2D gradient-based inversion. In comparison with the classic Cole-Cole model, it was found that for all the three re-parameterisations, the model parameters are less correlated with each other and, consequently, better resolved for both time-domain and frequency-domain data. The increase in model resolution is particularly significant for models that are poorly resolved using the classic Cole-Cole parameterisation, for instance, for low values of the frequency exponent or with low signal-to-noise ratio. In general, this leads to a significantly deeper depth of investigation for the \u3c6max, \u3c3max\u2033, and \u3c1min\u2033 parameters, when compared with the classic m0 parameter, which is shown with a field example. We believe that the use of reparameterisations for inverting field data will contribute to narrow the gap between induced polarization theory, laboratory findings, and field applications

Hypoxia-resistance heterogeneity in tumours: the impact of geometrical characterization of environmental niches and evolutionary trade-offs. A mathematical approach

In the study of cancer evolution and therapeutic strategies, scientific evidence shows that a key dynamics lies in the tumor-environment interaction. In particular, oxygen concentration plays a central role in the determination of the phenotypic heterogeneity of cancer cell populations, whose qualitative and geometric characteristics are predominant factors in the occurrence of relapses and failure of eradication. We propose a mathematical model able to describe the eco-evolutionary spatial dynamics of tumour cells in their adaptation to hypoxic microenvironments. As a main novelty with respect to the existing literature, we combine a phenotypic indicator reflecting the experimentally-observed metabolic trade-off between the hypoxia-resistance ability and the proliferative potential with a 2d geometric domain, without the constraint of radial symmetry. The model is settled in the mathematical framework of phenotype-structured population dynamics and it is formulated in terms of systems of coupled non-linear integro-differential equations. The computational outcomes demonstrate that hypoxia-induced selection results in a geometric characterization of phenotypic-defined tumour niches that impact on tumour aggressiveness and invasive ability. Furthermore, results show how the knowledge of environmental characteristics provides a predictive advantage on tumour mass development in terms of size, shape, and composition

30Cappa - The “Christmas” decree in kilometres

Following the COVID-19 pandemic emergency, in mid-December 2020 the Italian government introducedtravel restrictions during the Christmas holidays. In the implemented policies there was an exception:citizens of a municipality of up to 5,000 citizens can move within an area of 30 kilometers from their respectiveborders. This policy was used again in the following months to manage travel during the pandemic.30Cappa is a data visualization created by three civic hackers to give citizens the opportunity to understandthis policy (”cappa” is the pronunciation of the letter ”k” in Italian and means ”km”). The project consistsof a website where it is possible to receive information on the matter and the cartographic representationof the municipalities that correspond to the exception. The site has reached 350,000 unique visitors in twomonths and there has been a lot of talk about it in the media. This work highlights how the transformationof a policy into a tangible product such as a map created with the open data available, becomes an effectivetool to guide citizens and also to review the policies themselve

A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer

Hypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells

Hypoxia-related radiotherapy resistance in tumours: treatment efficacy investigation in an eco-evolutionary perspective

In the study of therapeutic strategies for the treatment of cancer, eco-evolutionary dynamics are of particular interest, since characteristics of the tumour population, interaction with the environment and effects of the treatment, influence the geometric and epigenetic characterization of the tumour with direct consequences on the efficacy of the therapy and possible relapses. In particular, when considering radiotherapy, oxygen concentration plays a central role both in determining the effectiveness of the treatment and the selective pressure due to hypoxia. We propose a mathematical model, settled in the framework of epigenetically-structured population dynamics and formulated in terms of systems of coupled non-linear integro-differential equations, that aims to catch these phenomena and to provide a predictive tool for the tumour mass evolution and therapeutic effects. The outcomes of the simulations show how the model is able to explain the impact of environmental selection and therapies on the evolution of the mass, motivating observed dynamics such as relapses and therapeutic failures. Furthermore it offers a first hint for the development of therapies which can be adapted to overcome problems of resistance and relapses