Evolving localizations in reaction-diffusion cellular automata

We consider hexagonal cellular automata with immediate cell neighbourhood and three cell-states. Every cell calculates its next state depending on the integral representation of states in its neighbourhood, i.e. how many neighbours are in each one state. We employ evolutionary algorithms to breed local transition functions that support mobile localizations (gliders), and characterize sets of the functions selected in terms of quasi-chemical systems. Analysis of the set of functions evolved allows to speculate that mobile localizations are likely to emerge in the quasi-chemical systems with limited diffusion of one reagent, a small number of molecules is required for amplification of travelling localizations, and reactions leading to stationary localizations involve relatively equal amount of quasi-chemical species. Techniques developed can be applied in cascading signals in nature-inspired spatially extended computing devices, and phenomenological studies and classification of non-linear discrete systems.Comment: Accepted for publication in Int. J. Modern Physics

Thermodynamic Losses in a Gas Spring: Comparison of Experimental and Numerical Results

Reciprocating-piston devices can be used as high-efficiency compressors and/or expanders. With an optimal valve design and by carefully adjusting valve timing, pressure losses during intake and exhaust can be largely reduced. The main loss mechanism in reciprocating devices is then the thermal irreversibility due to the unsteady heat transfer between the compressed/expanded gas and the surrounding cylinder walls. In this paper, pressure, volume and temperature measurements in a piston-cylinder crankshaft driven gas spring are compared to numerical results. The experimental apparatus experiences mass leakage while the CFD code predicts heat transfer in an ideal closed gas spring. Comparison of experimental and numerical results allows one to better understand the loss mechanisms in play. Heat and mass losses in the experiment are decoupled and the system losses are calculated over a range of frequencies. As expected, compression and expansion approach adiabatic processes for higher frequencies, resulting in higher efficiency. The objective of this study is to observe and explain the discrepancies obtained between the computational and experimental results and to propose further steps to improve the analysis of the loss mechanisms

Data-driven approaches for techno-economic assessment of waste heat recovery and utilisation in the industrial sector

The industrial sector is a critical element in the sustainability transition as it is currently the largest consumer of fossil fuels, and the consumption is forecasted to continue to increase. Approximately one-fifth of the total industrial primary energy consumption is wasted due to the lack of proven attractive schemes for effective recovery. When addressing the opportunities of industrial waste heat recovery (WHR), it is found that the feasibility depends on multiple factors, including the forms and capacities of the heat sources, the potential heat sinks, and the effectiveness, technological maturity, and economic impact of available technologies. Developing systematic approaches to identify optimal WHR options for different applications is key to effectively reduce plant-scale energy consumption. In particular, power consumption accounts for more than half of the industrial energy use, and its share is expected to grow with the expansion of electrification aspirations. In this paper, industrial WHR technologies are investigated, and tools are developed to understand the sustainability and techno-economic impact of integrating these technologies within industrial processes. We specifically propose a data-driven technology-agnostic approach to evaluate the use of heat engines, which can in practice be organic Rankine cycle (ORC) systems, and of thermally- driven (i.e., absorption) heat pumps in the context of industrial WHR for plant-scale power demand reduction. The scope of this work explores three pathways to achieving efficiency improvements in bulk chemicals plants, represented by olefins production facilities, which are: (i) direct onsite power generation; (ii) enhancement of existing power generation processes; and (iii) reduction in power consumption by compressor efficiency improvements through waste-heat-driven cooling. The techno-economic performance of these technologies is assessed, with particular attention to industrial facilities that reside in hot climates, using fine-tuned technology-agnostic thermodynamic and market-based costing models. Finally, decision-aiding performance maps are derived by varying the quantity and the quality of waste-heat sources and heat sinks, offering application- specific guidelines for selecting appropriate waste-heat recovery schemes. These findings reveal valuable factors for selecting such integration schemes for various industries and scenarios

Dynamic control strategies for a solar-ORC system using first-law dynamic and data-driven machine learning models

In this study, we developed and assessed the potential of dynamic control strategies for a domestic scale 1-kW solar thermal power system based on a non-recuperated organic Rankine cycle (ORC) engine coupled to a solar energy system. Such solar-driven systems suffer from part-load performance deterioration due to diurnal and inter-seasonal fluctuations in solar irradiance and ambient temperature. Real-time control strategies for adjusting the operating parameters of these systems have shown great potential to optimise their transient response to time-varying conditions, thus allowing significant gains in the power output delivered by the system. Dynamic model predictive control strategies rely on the development of computationally efficient, fast-solving models. In contrast, traditional physics-based dynamic process models are often too complex to be used for real-time controls. Machine learning techniques (MLTs), especially deep learning artificial neural networks (ANN), have been applied successfully for controlling and optimising nonlinear dynamic systems. In this study, the solar system was controlled using a fuzzy logic controller with optimised decision parameters for maximum solar energy absorption. For the sake of obtaining the optimal ORC thermal efficiency at any instantaneous time, particularly during part-load operation, the first-law ORC model was first replaced by a fast-solving feedforward network model, which was then integrated with a multi-objective genetic algorithm, such that the optimal ORC operating parameters can be obtained. Despite the fact that the feedforward network model was trained using steady-state ORC performance data, it showed comparable results compared with the first-principle model in the dynamic context, with a mean absolute error of 3.3 percent for power prediction and 0.186 percentage points for efficiency prediction

Techno-economic comparison of hydrogen- and electricity-driven technologies for the decarbonisation of domestic heating

Sustainable transition pathways currently being proposed for moving away from the use of natural gas and oil in domestic heating focus on two main energy vectors: electricity and hydrogen. The former transition would most likely be implemented using electric vapour-compression heat pumps, which are currently experiencing market growth in many industrialised countries. Electric heat pumps have proven to be an efficient alternative to gas boilers under certain conditions, but their techno-economic potential is highly dependent on the local climate conditions. Hydrogen-based heating systems, which could potentially utilise existing natural gas infrastructure, are being proposed as providing an attractive opportunity to maximise the use of existing assets to facilitate the energy-system transition. In this case, hydrogen can substitute natural gas in boilers or in thermally driven absorption heat pumps. Both heating system transition pathways may involve either installing new technologies at the household level or producing heat in centralised hubs and distributing it via district-heating systems. Although the potential of hydrogen in the context of heating decarbonisation has been explored in the past, a comprehensive comparison of electricity- and hydrogen-driven domestic heating options is lacking in literature. In this paper, a thermodynamic and economic methodology is developed to assess the competitiveness of a domestic-scale ammonia-water absorption heat pump driven by heat from a hydrogen boiler compared to a standalone hydrogen boiler, a classic vapour-compression heat pump and district heating, all from a homeowner’s perspective. Using a previously developed electric heat pump model, the different systems are compared for various climate conditions and fuel-price scenarios under a unified framework. The coefficient of performance of the absorption heat pump system under design conditions and the total system cost are found to be 1.4 and £5400, respectively. Comparing the annualised total costs of the options under consideration, it is shown that, assuming the future price of hydrogen for domestic end-users can be below 0.12 £/kWh, absorption heat pumps and hydrogen boilers can become competitive domestic heating technologies, and otherwise, electrification and the use of vapour-compression heat pump will be preferred

FACTS, which advantages for the modern electrical networks?

The energy market liberalization requires using the actual transportation network in a more flexible and more efficient way. What is needed are strategies and facilities that will improve the energy flux sharing, as well as the maintaining of the node voltages. These goals can only be partially reached in the conventional networks. However, the FACTS are offering a very promising solution. The UPFL (Unified Power Flow Controller) will be briefly presented as an example of FACTS, and its behaviour will be illustrated with simulations

A modular software package for the analysis of power networks and electrical drives

This paper deals with a software package for the numerical analysis in transient and steady-state modes of power electrical networks or variable speed drives with arbitrary topologies. The package is composed of a series of units, each representing a specific cell in the network : voltage supply, electrical machine, mechanical system, transmission line, circuit-breaker, phase shifting transformer, static converter with control and command organ, regulator, etc. SIMSEN is highly flexible and efficient. It is implanted on microcomputer or on workstation. The network or the variable speed drive to be simulated is assembled using a graphic input interface by adequatly choosing and linking the building units, so as to fulfill a desired topology. An existing system may be easily extended or modified. A simple procedure can be used in order to define a new unit or to modify an existing one. The initial conditions of operation can be partly or entirely specified by the user. A transient mode of operation may include several successive perturbations. The simulation results are displayed through an efficient graphic interface. An original feature of SIMSEN is its ability to analyse electrical networks involving semi-conductors (diodes rectifiers, thyristor or GTO current converters, voltage inverters, etc.). Thus, systems having a complex structure can be simulated. Practical examples of applications to such systems constitute the main part of this paper : constant or variable speed groups involving induction and synchronous machines, HVDC networks, reactive power static converter SVC. These examples show how SIMSEN can be used for an optimized design of complex networks

Once-Weekly Exenatide Versus Once- or Twice-Daily Insulin Detemir: Randomized, open-label, clinical trial of efficacy and safety in patients with type 2 diabetes treated with metformin alone or in combination with sulfonylureas

OBJECTIVEdThis multicenter, open-label, parallel-arm study compared the efficacy and safety of exenatide once weekly (EQW) with titrated insulin detemir in patients with type 2 diabetes inadequately controlled with metformin (with or without sulfonylureas). RESEARCH DESIGN AND METHODSdPatients were randomized to EQW (2 mg) or detemir (once or twice daily, titrated to achieve fasting plasma glucose #5.5 mmol/L) for 26 weeks. The primary outcome was proportion of patients achieving A1C #7.0% and weight loss $1.0 kg at end point, analyzed by means of logistic regression. Secondary outcomes included measures of glycemic control, cardiovascular risk factors, and safety and tolerability. RESULTSdOf 216 patients (intent-to-treat population), 111 received EQW and 105 received detemir. Overall, 44.1% (95% CI, 34.7–53.9) of EQW-treated patients compared with 11.4% (6.0–19.1) of detemir-treated patients achieved the primary outcome (P , 0.0001). Treatment with EQW resulted in significantly greater reductions than detemir in A1C (least-square mean 6 SE, 21.30 6 0.08% vs. 20.88 6 0.08%; P , 0.0001) and weight (22.7 6 0.3 kg vs. +0.8 6 0.4 kg; P , 0.0001). Gastrointestinal-related and injection site–related adverse events occurred more frequently with EQW than with detemir. There was no major hypoglycemia in either group. Five (6%) patients in the EQW group and six (7%) patients in the detemir group experienced minor hypoglycemia; only one event occurred without concomitant sulfonylureas (detemir group). CONCLUSIONSdTreatment with EQW resulted in a significantly greater proportion of patients achieving target A1C and weight loss than treatment with detemir, with a low risk of hypoglycemia. These results suggest that EQW is a viable alternative to insulin detemir treatment in patients with type 2 diabetes with inadequate glycemic control using oral antidiabetes drugs