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Low Grade Heat Driven Multi-effect Distillation and Desalination describes the development of advanced multi-effect evaporation technologies that are driven by low grade sensible heat, including process waste heat in refineries, heat rejection from diesel generators or microturbines, and solar and geothermal energy. The technologies discussed can be applied to desalination in remote areas, purifying produced water in oil-and-gas industries, and to re-concentrate process liquor in refineries.
This book is ideal for researchers, engineering scientists, graduate students, and industrial practitioners working in the desalination, petrochemical, and mineral refining sectors, helping them... 

This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation?
Here, the term "non-parametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data.
The book provides an overview of methods that have been developed for the analysis of... 

Increasing concerns about unbridled fossil fuel consumption and a growing generation of municipal solid waste (MSW) in Iran and related environmental challenges require adoption of more sustainable approaches in energy and waste management. Energy recovery from waste can play an important role to achieve environmentally sustainable development that has been defined for the country as a main policy. Iran’s growing urban population generates massive amounts of MSW that mostly end up in landfills. A population of 75 million is generating 50,000 tonnes of MSW every day that mostly are buried in landfills. Detrimental emissions from landfills and many other long-lasting environmental issues have... 

This study aims to investigate the micromixing performance of three basic types of spatial shaped micromixers. New configurations of T, Y, and oriented Y-spatial mixers were designed with change in the angles of the confluence and the outlet channel to achieve the efficient micromixing. These micromixers offer advantages that are not attainable with the typical types of these mixers. Experimental tests were carried out in the laminar flow regime and the mixing efficiency was evaluated using Villermaux/Dushman test reaction. The geometries of the channels were cylindrical with the length of 30 mm and the diameter of 800 μm. The experimental results show that the angle of outlet channel has a... 

This study aims to investigate the micromixing performance of three basic types of spatial shaped micromixers. New configurations of T, Y, and oriented Y-spatial mixers were designed with change in the angles of the confluence and the outlet channel to achieve the efficient micromixing. These micromixers offer advantages that are not attainable with the typical types of these mixers. Experimental tests were carried out in the laminar flow regime and the mixing efficiency was evaluated using Villermaux/Dushman test reaction. The geometries of the channels were cylindrical with the length of 30 mm and the diameter of 800 μm. The experimental results show that the angle of outlet channel has a... 

In Chaps. 16 – 21 we address a central question in the field of complex systems: Given a fluctuating (in time or space), sequentially uni- or multi-variant measured set of experimental data (even noisy data), how should one analyse the data non-parametrically, assess their underlying trends, discover the characteristics of the fluctuations, including diffusion and jump parts, and construct stochastic evolution equation for the data? © 2019, Springer Nature Switzerland AG  

In this chapter we present the steps of reconstruction procedure for writing down the Langevin and jump diffusion stochastic dynamical equations for uni- and bivariate time series, sampled with time intervals τ. © 2019, Springer Nature Switzerland AG  

In this chapter, we present the details of Kramers–Moyal (KM) expansion and prove the Pawula theorem. The Fokker–Planck equation is then introduced and its short-term propagator is presented. Finally, we derive the master equation from the Chapman–Kolmogorov equation. © 2019, Springer Nature Switzerland AG  

In this chapter Itô and Stratonovich calculi are introduced and we prove the Itô lemma and describe Itô calculus for multiplicative noise. Finally Itô-Taylor expansion will be given for white noise-driven Langevin dynamics. © 2019, Springer Nature Switzerland AG  

In this chapter we present three examples of the Langevin dynamics, namely, the Anderson–Kubo, Ornstein–Uhlenbeck, and Black–Scholes processes. We will use the Itô interpretation that provides us with a powerful tool for dealing with such linear and nonlinear stochastic processes analytically. © 2019, Springer Nature Switzerland AG  

In this chapter we introduce the Langevin equation and Wiener process. All the statistical properties of Wiener process will be presented and discussed. © 2019, Springer Nature Switzerland AG  

Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with... 

In this chapter we study stochastic properties of spatially- and temporally-disordered structures, such as turbulence and rough surfaces, or temporal fluctuations of given time series, in scale. Experimental observables include the field increments, such as the difference in the velocity field between two points separated by a distance r, or difference of time series in a time lag r. Therefore, the lag r can be either spatial distance or a time interval. The change of the increments’ fluctuations as a function of the scale r can then be viewed as a stochastic process in a length or time scale and can, quite often, after pioneering work by Friedrich & Peinke, be mapped onto the mathematical...