Instantaneous Consumption Research Articles

A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills

The design of renewable-based and collective energy systems requires consumption data with fine temporal and spatial resolution. Despite the increasing deployment of smart meters, obtaining such data directly from measurements can still be challenging, particularly when attempting to gather historical data over a reasonable period for many end users. As a result, there is a need for models to simulate or predict these consumption data (e.g., hourly load profiles). Typically, these models rely on numerous specific and detailed observations, such as load type, household size for residential customers, or operating hours for commercial ones. However, gathering this level of detail becomes increasingly difficult as the number and diversity of end users increase. Therefore, this paper proposes a data-driven approach to predict hourly load profiles of heterogeneous end users using only their monthly time-of-use electricity bills as inputs. We create a training set using a limited number of hourly measurements from diverse categories of end users and, differently from other approaches aimed at classifying the end users, we develop a regression model to map monthly electricity bills to typical load profiles. Experimental results using one year of data from various end-user categories demonstrate an average normalized mean absolute error of approximately 26% for instantaneous consumption and less than 4% for time-of-use values. Comparative analysis with standard load profiles and a two-step data-driven approach based on classification reveals that our proposed method outperforms the others in terms of prediction accuracy and statistical metrics.

Диагностирование газовоздушного тракта тепловозного дизеля с использованием интеллектуального классификатора

Purpose: Efficiency increase of operational monitoring of technical state of gas-air tract (GAT) of a locomotive diesel, assessment of possibility of using locomotive regular measuring tools to monitor diesel GAT technical state; feasibility justification for expanding the list of GAT parameters that are monitored by locomotive board diagnostics. Methods: An intelligent classifier based on Support Vector Machine (SVM) algorithm is used to determine the current technical condition (TC) class of GAT. Training sample of 96 item volume for eight classes of GAT TC was formed using “Diesel-RK” software complex. Classifier structure three variants were studied, differing in the set of input parameter vector components: regular, with two inputs (pressure increase degree in a supercharger and exhaust gas temperature before a turbine), expanded, with additional control of turbocharger rotor rotation speed, and proposed, with control of air instantaneous consumption by a diesel instead. Model quality was assessed by cross-validation on five fragments and by classification results for test sample which wasn’t used while training. Results: Classification accuracy of testing sample by classifier with input parameter regular set doesn’t exceed 41%. The expansion of being controlled list of parameter on account of inclusion therein of turbocharger rotor rotation speed does not noticeably affect diagnostic system efficiency, increasing diagnostic accuracy only till 43%. At the same time, rotor rotation speed replacement with air instantaneous consumption rises testing sample classification accuracy till 91%. Practical significance: The effectiveness of the use of machine learning methods for operative monitoring of diesel GAT technical state at condition of optimization of the list of diesel parameters, controlled by locomotive board diagnostics tools, is confirmed. With the purpose to increase control efficiency it is recommended to include diesel instantaneous air consumption into the list with parallel exclusion from their of a turbocharger rotor rotation frequency.

Optimal investment, consumption, and life insurance strategies under a mutual-exciting contagious market

We study an optimisation problem of a household under a contagious financial market. The market consists of a risk-free asset, multiple risky assets and a life insurance product. The clustering effect of the market is modelled by mutual-excitation Hawkes processes where the jump intensity of one risky asset depends on both the jump path of itself and the jump paths of other risky assets in the market. The labor income is generated by a diffusion process which can cover the Ornstein-Uhlenbeck (OU) process and the Cox-Ingersoll-Ross (CIR) model. The goal of the household is to maximise the expected utilities from both the instantaneous consumption and the terminal wealth if he survives up to a fixed retirement date. Otherwise, a lump-sum heritage will be paid. The mortality rate is modelled by a linear combination of exponential distributions. We obtain the optimal strategies through the dynamic programming principle and develop an iterative scheme to solve the value function numerically. We also provide the proof of convergence of the iterative method. Finally, we present a numerical example to demonstrate the impact of key parameters on the optimal strategies.

Context-Aware Authentication with Dynamic Credentials using Electricity Consumption Data

AbstractIndustrial IoT (IIoT) era is evolving rapidly in parallel to the progress in Industry 4.0, which leads factories to increase the engagement with external parties through different communication infrastructures. This brings a larger attack surface and requires the development of new security solutions suitable for IIoT systems. Authentication is a key enabler to prevent the attacks that come from the interactions with the outside world components. Though there are proposed authentication schemes for IoT or machine-to-machine (M2M) applications, they cannot be readily applied for IIoT since manufacturing machines have different features and requirements than generic IT and IoT devices. In this paper, context information is proposed to be used for enhancing the authentication process in the IIoT, where instantaneous electricity consumption measured by smart meters is the main context information used as a dynamic authentication credential. Besides, in our method, existing smart meter infrastructure is utilized for both exchanging credentials over an industrial network and verification of the credentials by trusted components like Supervisory Control and Data Acquisition (SCADA) or Energy Management System (EMS). The proposed method also allows the use of other context information as authentication credentials such as temperature and humidity collected by sensors in the manufacturing environment.

Instantaneous gas flow rate estimation in the gas-gathering system flowline by indirect measurement method

Implementation of modern automation systems on the northern gas fields located at the final stage of operation, is unprofitable economically and is not always possible technically. At the same time, the competent operation of such deposits requires constant monitoring for the state of gas wells and the shelves of the gas transmission network. In particular, the pressure and temperature is obligatory at the mouth of the well and at the end of the shelf. Also an important parameter from the point of view of regulating the modes of the wells is the instantaneous flow rate of the shelf, which is currently not measured at the old deposits. The article proposes to assess the Instantaneous consumption by an indirect method, according to the values of pressure and temperature. The algorithm for calculating the consumption is proposed, the testing of which at the Urengoy gas condensate field confirmed its adequacy. The monitoring system of technological parameters is built into gas fishery ACS. The algorithm visualization is implemented in the SCADA HMI / Cimplicity software package.

Biased energy efficiency perception based on instantaneous consumption displays – Indication for heuristic energy information processing

Instantaneous consumption displays (ICDs) can be used as central information source to perceive the energy efficiency of manoeuvre-level driving. A key question is whether drivers who use ICDs can accurately derive energy efficiency differences of different driving strategies based on ICDs. There is reason to assume that drivers' consumption judgements may be biased, similar to driving-related phenomena like the time-saving bias. Therefore, the aim of the present research was to examine drivers’ accuracy in deriving average consumption from dynamic ICD sequences. Participants viewed videos of a schematic ICD in a controlled experiment where the maximum instantaneous consumption systematically varied over time. Participants (N = 55) overestimated the average consumption values. The empirical ranking of the sequences did significantly correlate with the heuristic but not with the correct efficiency ranking. The current study incorporated multilevel modelling due to the nested structure of the data. The estimation difference was greater with higher peak height and shorter peak duration. The effect of peak height on estimation difference weakened with longer peak duration. In sum, the results indicate that ICDs can create biased perceptions of energy efficiency and that drivers seem to use simplifying heuristics. Knowledge and affinity for technology interaction appear to relate to biased estimations, whereas the intensity of prior experience with consumption displays seems irrelevant. Further studies should test other interfaces with debiasing potential such as manoeuvre-based aggregation or fading-trace approaches. Moreover, studies are needed that enable modelling of the effects of more natural temporal-spatial visual attention distribution (e.g. in a driving simulator setting).

Efficient Formulations and Decomposition Approaches for Power Peak Reduction in Railway Traffic via Timetabling

Over the last few years, optimization models for the energy-efficient operation of railway traffic have received more and more attention, particularly in connection with timetable design. In this work, we study the effect of load management via timetabling. The idea is to consider trains as time-flexible consumers in the railway power supply network and to use slight shifts in the departure times from the stations to avoid too many simultaneous departures. This limits peak consumption and can help to improve the stability of the power supply. To this end, we derive efficient formulations for the problem of an optimal timetable adjustment based on a given timetable draft, two of which even allow for totally unimodular polyhedral descriptions. The proper choice of the objective function allows the incorporation of the priorities of either the train operating companies or the infrastructure manager. These include the avoidance of large peaks in average or instantaneous consumption and the improved use of recuperated braking energy. To solve the arising optimization models efficiently, we develop specially tailored exact Benders decomposition schemes that allow for the computation of high-quality solutions within a very short time. In an extensive case study for German railway passenger traffic, we show that our methods are capable of solving the problem on a nationwide scale. We see that the optimal adjustment of timetables entails a tremendous potential for reducing energy consumption.

Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation

Ensembles of thermostatically controlled loads (TCL) provide a significant demand response reserve for the system operator to balance power grids. However, this also results in the parasitic synchronization of individual devices within the ensemble leading to long post-demand-response oscillations in the integrated energy consumption of the ensemble. The synchronization is eventually destructed by fluctuations, thus leading to the (pre-demand response) steady state; however, this natural desynchronization, or relaxation to a statistically steady state, is too long. A resolution of this problem consists in measuring the ensemble’s instantaneous consumption and using it as a feedback to stochastic switching of the ensemble’s devices between on- and off-states. A simplified continuous-time model showed that carefully tuned nonlinear feedback results in a fast (super-) relaxation of the ensemble energy consumption. Since both state information and control signals are discrete, the actual TCL devices operation is space–time quantized, and this must be considered for realistic TCL ensemble modeling. Here, assuming that states are characterized by indoor temperature (quantifying comfort) and air conditioner regime (on, off), we construct a discrete model based on the probabilistic description of state transitions. We demonstrate that super-relaxation holds in such a more realistic setting, and that while it is stable against randomness in the stochastic matrix of the quantized model, it remains sensitive to the time discretization scheme. Aiming to achieve a balance between super-relaxation and customer’s comfort, we analyze the dependence of super-relaxation on details of the space–time quantization, and provide a simple analytical criterion to avoid undesirable oscillations in consumption.

Birth of the Binge: Serial TV and the End of Leisure by Dennis Broe

Reviewed by: Birth of the Binge: Serial TV and the End of Leisure by Dennis Broe Suryansu Guha Birth of the Binge: Serial TV and the End of Leisure Dennis Broe Wayne State UP, 2019. 312 pages. $34.99 paperback. Dennis Broe's book is a new historicist reading of the ubiquitous cultural phenomenon of 'binge watching' or instantaneous consumption of television shows. Broe portrays this phenomenon as an inevitable result of the serialization of television shows. The rise of seriality in television series like Lost or Walking Dead, the book argues, is different from the kind of seriality that television shows used to have in the age of broadcast television. Although the book argues that the narrative archetypes of television shows remain the same, it claims that the shows themselves no longer are created for a fluctuating audience. As a result, episodes do not have to be as self-contained as they used to be. Thus, what Broe is doing in this book, by and large, is looking at how more macro political, economic and lifestyle changes in the world shape and influence what was being produced for screen. Drawing heavily from Bernard Stiegler's work, on the one hand, and Adorno's on the other, the book tries to understand how from the 80s onwards and with the rise of neoliberal economic policies certain processes were set in motion and how the "products of the culture industry bore the imprint of [those] process[es]" (28). By conducting a rigorous close textual analysis of a wide range of shows, the book reinterprets them (and sometimes movies like The Avengers) in light of neoliberal policies, growing wealth inequality, and the precarity of work conditions. The first part of the book shows how television consumerism has been very quick to adapt and appropriate the new platform to propagate its hyperindustrial modes of leisure. The first four chapters show how neoliberal ideologies are coded deep into the structures of these shows and how they are constantly trying to produce a neoliberal subject by producing in him "autism and addiction, the psychological conditions par excellence of hyperindustrial capital" (97). The second part of the book, consisting of the fifth and the sixth chapters, delves deeply into philosophical deliberations over seriality, offering a reconsideration of this term itself and what it entails as far as contemporary television is concerned. Chapter five starts with a Nietzschean understanding of the world and by extension television shows, as an eternal process of recurrence where time does not "evolve or advance but just circulates" (139). The counterpoint to this is posed via Hegel, Marx, and Lukacs who understand repetition through dialectics or returning with an understanding of "capitalist relations as a whole" (142). These two conceptions of time, the Hegelian and Marxist totality as opposed to the Nietzschian eternal return, are explored in the rest of the chapter through literature, comic books, radio, and, in the sixth chapter, film. The third and the final part of the book explores the emancipatory potential and possibilities of resistance that can come from within these hyperindustrial conventions and modes of consumption. Broe locates the site of this emancipation or resistance in the showrunner and the storyteller who can "carve out an autonomous space," "build engagement with social discourse," and "actualize the power of the medium" (211). The book locates fragments of subversive possibilities in a range of television shows like Dollhouse, Firefly, and Buffy. In sum, the book makes an Althusserian argument about how seriality and binge watching is a part of the hyperindustrialist, neoliberal mode of interpellation and subject formation, but also how the possibilities of counter-discursive formation that can upend the status quo are present within these modes. Though it at times threatens to subsume all of contemporary television narratives into the larger socio-political macro-narrative that Broe proposes, Broe's book offers a fascinating reading of how hyperindustriality informs not just the patterns of production of screen industries but also those of viewership. [End Page 48] While one can agree wholeheartedly with the overall argument of this book, it invites questions about a more concrete historical periodization and trajectory of seriality that might underpin the titular...

Energy Allocation Strategies for Common Property Load Connected to Shared Solar and Battery Storage Systems in Strata Apartments

Common property (CP) is a significant consumer of electricity in apartment buildings. Although some apartments in Australia have adopted shared microgrid configurations to offset grid consumption, the characteristics and load patterns of CP are rarely discussed due to lack of available data. As common areas normally constitute part of owner corporations, energy distribution in these premises requires attention. This paper presents empirical analysis of the CP load connected to shared solar and battery storage for three apartment complexes located in Perth Australia. Load patterns for CP over a defined dataset period were analyzed, and grid usage reduction was examined by implementing and comparing three energy allocation strategies based on surplus energy utilization. The findings indicated significant grid usage reduction for CP load in different apartments after implementation of three strategies. Instantaneous consumption decreased 72%, and surplus allocation strategy reduced 91%, while consumption-based allocation reduced 76%, of grid electricity. Moreover, consumption-based allocation offered improved cost benefits compared to the other two strategies. The results further revealed the usefulness of energy allocation and effectiveness of surplus energy utilization. Based on outcomes, the strategies provide consolidation with conventional energy trading mechanisms and broadly link to the virtual power plant concept for coordinating energy flows between multiple generators.

An Extended Mean Field Game for Storage in Smart Grids

We consider a stylized model for a power network with distributed local power generation and storage. This system is modeled as a network connection of a large number of nodes, where each node is characterized by a local electricity consumption, has a local electricity production (photovoltaic panels for example) and manages a local storage device. Depending on its instantaneous consumption and production rate as well as its storage management decision, each node may either buy or sell electricity, impacting the electricity spot price. The objective at each node is to minimize energy and storage costs by optimally controlling the storage device. In a noncooperative game setting, we are led to the analysis of a nonzero sum stochastic game with N players where the interaction takes place through the spot price mechanism. For an infinite number of agents, our model corresponds to an extended mean field game. We are able to compare this solution to the optimal strategy of a central planner and in a linear quadratic setting, we obtain and explicit solution to the extended mean field game and we show that it provides an approximate Nash equilibrium for N-player game.

A correlation analysis of the spot market prices of the Romanian electricity sector

Abstract The energy sector is particularly important in the national economy as a whole, while the electricity sector is its main component. The developments in this area have strong economic and social repercussions. This article makes an overview of the electricity sector, illustrating the monthly evolution of consumption and production of instantaneous electricity in Romania in the past few years. Further on, the paper presents the monthly evolution of instantaneous consumption and production of electricity in Romania, the annual structure of electricity production and the depiction of some basic indicators of centralized electricity markets managed by OPCOM. The quantitative analysis presents the monthly evolution of weighted average prices on the competitive markets managed by OPCOM and a linear correlation analysis of the spot market prices in the Eastern European region. The conclusions of the article are in line with the research in the field and show that the electricity markets have registered important evolutions in Romania, both in terms of size and prices, evolutions based on both structural and behavioral factors. It also showed that spot market prices had relatively similar developments over the period analyzed in several countries in the region, with price correlations that seem to be even stronger as the interconnection of electricity grids is higher. Therefore, the results confirm the economic theory, namely that increasing the interconnection of electricity markets can lead to energy prices being brought closer, but also to mitigating the shocks affecting these markets.

Predicting the consumption speed of a premixed flame subjected to unsteady stretch rates

The stretched laminar flame model provides a convenient approach to embed realistic chemical kinetics when simulating turbulent premixed flames. When positive-only periodic strain rates are applied to a laminar flame there is a notable phase lag and diminished amplitude in heat release rate. Similar results have being observed with respect to the other component of stretch rate, namely the unsteady motion of a curved flame when the stretch rates are periodic about zero. Both cases showed that the heat release rate or consumption speed of these laminar-premixed flames vary significantly from the quasi-steady flamelet model. Deviation from quasi-steady behavior increases as the unsteady flow time scale approaches the chemical time scale that is set by the stoichiometry. A challenge remains in how to use such results predictively for local and instantaneous consumption speed for small segments of turbulent flames where their unsteady stretch history is not periodic.This paper uses a frequency response analysis as a characterization tool to simplify the complex non-linear behavior of premixed methane air flames for equivalence ratios from 1.0 down to 0.7, and frequencies from quasi-steady up to 2000 Hz using flame transfer functions. Various linear and nonlinear models were used to identify appropriate flame transfer functions for low and higher frequency regimes, as well as extend the predictive capabilities of these models. Linear models were only able to accurately predict the flame behavior below a threshold of when the fluid and chemistry time scales are the same order of magnitude. Other proposed transfer functions were tested against arbitrary multi-frequency stretch inputs and were shown to be effective over the full range of frequencies.

Security and privacy aware data aggregation on cloud computing

The use of cloud computing has become common due to advantages such as low cost and sizing of computing resources according to demand. However, it also raises security and privacy concerns, because critical data – for example, in IoT applications – are stored and processed in the cloud. This paper proposes a software architecture that supports multiple approaches to secure data aggregation. For validation purposes, this software architecture was used in the development of applications for smart grids, computing instantaneous consumption of a region and the monthly bill of an individual consumer. The consumption data can be collected by smart meters, enabling consumers to reduce electricity costs through close monitoring. However, such data may reveal sensitive information if no privacy techniques are applied. Therefore, the proposed software architecture proved to be viable from experiments with techniques such as homomorphic encryption and hardware security extensions (Intel SGX).

On discounting and voting in a simple growth model

In dynamic resource allocation models, the non-existence of voting equilibria is a generic phenomenon due to the multi-dimensionality of the choice space even if agents are heterogeneous only in their discount factors. Nevertheless, at each point in time there may exist a “median voter” whose preferred instantaneous consumption rate is supported by a majority of agents. Based on this observation, we propose an institutional setup (“intertemporal majority voting”) in a Ramsey-type growth model with common consumption and heterogeneous agents, and show that it provides a microfoundation of the choice of the optimal consumption stream of the “median” agent. While the corresponding intertemporal consumption stream is in general not a Condorcet winner among all feasible paths, its induced instantaneous consumption rates receive a majority at each point in time in the proposed intertemporal majority voting procedure. We also provide a characterization of stationary voting equilibria in the case where agents may differ not only in their time preferences, but also in their felicity functions.

The Direct Quantitative Measurement of In-Situ Burn (ISB) Rate and Efficiency

ABSTRACT In-situ burning (ISB) is an important tool to remove oil from the environment. During ISB, it is important to know the volume reduction of oil for the overall accounting of the spilled oil, as a metric for operational decisions, and to account for the ISB portion of the oil budget. The burn rate depends on the type of oil, degree of emulsification and weathering, estimated thickness, weather conditions, and size of the burn area. Furthermore, each spill has a unique physical environment and oil properties that affect burn efficiency and rate. The volume of oil consumed during ISB is typically computed using a manual, coarse, time integration of the instantaneous burn area based on visual observations and a characteristic burn rate. The area is typically estimated in the field using known boom geometry and visual inspection of the fire-water interface, and recorded manually. We have developed methods to measure the instantaneous consumption of burning oil and thus the oil burn rate by integrating direct measurements of thickness using acoustics sensors in the water under the slick with direct measurements of the area of the burning oil using infrared and visible light images from cameras above the burning oil. Data were collected during the burning of several oils and petroleum products including ANS, rock, diesel, and hexane. The acoustic thickness measurement took into account the high temperature gradient in the oil and combined with multi camera automated burn area estimates yielded an instantaneous measurement of the volume of oil consumed while burning. We were able to identify the buildup of the burn, the active burning phase, and in the case of confined burns the vigorous burning phase. Knowing the instantaneous thickness and surface area during burning allowed us to directly calculate the burn rate and to study the dynamics of ISB. We are working on validating the burn rate and efficiency with direct measurements of the weight of the oil and residue before, during, and after burning. The authors believe these are the first direct measurements of slick thickness using acoustics during ISB.

On discounting and voting in a simple growth model

In dynamic resource allocation models, the non-existence of voting equilibria is a generic phenomenon due to the multi-dimensionality of the choice space even if agents are heterogeneous only in their discount factors. Nevertheless, at each point in time there may exist a “median voter” whose preferred instantaneous consumption rate is supported by a majority of agents. Based on this observation, we propose an institutional setup (“intertemporal majority voting”) in a Ramsey-type growth model with common consumption and heterogeneous agents, and show that it provides a microfoundation of the choice of the optimal consumption stream of the “median” agent. While the corresponding intertemporal consumption stream is in general not a Condorcet winner among all feasible paths, its induced instantaneous consumption rates receive a majority at each point in time in the proposed intertemporal majority voting procedure. We also provide a characterization of stationary voting equilibria in the case where agents may differ not only in their time preferences, but also in their felicity functions.

Effect of Removing Indoor HCHO by Several Korean Foliage Plants

The objective of this study is to study the air cleaning ability of indoor plants and to find easier ways of its application. Carbon dioxide, the index of gas pollutant and formaldehyde, found in almost every indoor equipment were chosen to implement the air cleaning experiments. Selected plants in this study were 6 types; Ficus benjamina, Epipremnum aureum, Chamaedorea elegans, Fatsia japonica, Spathiphyllum spp. and Hedera helix. Measuring photosynthesis and breathing volume of each plant for 24 hours, total volume of carbon dioxide reduction for each plant was calculated; Ficus benjamina 49ppm, Epipremnum aureum 99ppm, Chamaedorea elegans 34ppm, Fatsia japonica 123ppm, Spathiphyllum spp. 115ppm, and Hedera helix 42ppm (leaf area unit: 1000Cm2). The time frame, in which the maximum instantaneous consumption of carbon dioxide was shown, was different for each plant; in most cases, the highest absorption rate of carbon dioxide was seen in 0.67~1.54 mW/Cm3 of light quantity. Injecting low concentration formaldehyde (250ppb) and comparing control group in darkened chamber, total volume of formaldehyde reduction for each plant was calculated; Ficus benjamina 128.6ppb, Epipremnum aureum 152ppb, Chamaedorea elegans 127.8ppb, Fatsia japonica 165.9ppb, Spathiphyllum spp. 156ppb, Hedera helix 115ppb (leaf area unit: 1000Cm2). Using statistical program SPSS 7.0, correlation analysis regarding instantaneous consumption volume of carbon dioxide and formaldehyde for each plant was implemented. Thanks to the conversion formula stated below, concentration of formaldehyde in indoor air and purification power of each plant were easily calculated. For 250ppb of formaldehyde, Y(HCHO consumption rate of all plants, ppb) = 0.387X (CO2 consumption rate of all plants, ppm) + 0.758. It was confirmed that plants can reduce indoor air pollutant from the simple fact; plants must absorb gas pollutants in the process of photosynthesis. Although it takes longer time to eliminate indoor air pollutants than mechanical air cleaners, it is the most environment-friendly method; less costs, continuous air purification and making no secondary pollutants.

Effect of vehicular technology on energy consumption and emissions

Sustainable development requires inter al the reduction of energy consumption and of traffic-induced pollutant emissions. Hybrid electric vehicles (HEVs) are one of the most promising approaches to decrease emissions. This paper considers the influence of hybridization of transport on energy consumption and emissions on single lanes of road traffic. We have developed a micro-simulation tool which integrates instantaneous consumption and emission models. We have modelled microscopic behaviour of vehicles using a full velocity difference model for longitudinal moving. Then, we have used two macroscopic energy consumption models (COPERT and HBEFA) and an instantaneous energy-consumption model concerning the conventional Internal Combustion Engine (ICE) vehicle to illustrate the relevance of microscopic modelling of energy consumption. Furthermore, we have compared the energy consumption of the HEV Toyota Prius with that of the conventional ICE vehicle. An emission model emissions from traffic (EMIT) was also implemented and extended in order to estimate HEV emissions. The model is used to quantify CO2 and CO emissions for the HEV Toyota Prius and the conventional ICE vehicle. Moreover, we have studied the influence of fleet hybridization level on energy consumption for congested and uncongested traffic flow state. HEVs can offer major environmental improvements as well as substantial reductions of energy consumption and road traffic emissions. Hybridization is a relevant solution to reduce energy consumption and emissions.

Synthesis of Vildagliptin Utilizing Continuous Flow and Batch Technologies

The preparation and utilization of the Vilsmeier reagent (VR) is well-known in the literature with its usefulness and scope being frequently demonstrated in organic synthesis. However, it is an irritant and has a high thermal energy of decomposition; consequently, these factors lead to operational issues on larger scale which suggests approaches whereby the reagent is not isolated. Herein, we report the in-line formation and instantaneous consumption of VR utilizing both conventional batch and flow technologies. The approach is demonstrated by way of the synthesis of Vildagliptin, thereby mitigating potential safety and hygiene hazards.