Time Slice Model Research Articles

KPLS-Based Mach Number Prediction for Multi-Mode Wind Tunnel Flow System

In wind tunnels, the Mach number needs to be precisely monitored and controlled. It is difficult to obtain the Mach number directly online, especially when the wind tunnel system is operating in multiple modes. To deal with this problem, a Mach number prediction algorithm based on the kernel partial least squares method is proposed for multi-mode wind tunnel systems. First, in order to reflect real-time changes, the time-slice partial least squares regression method is adopted. Then, in order to enable the model to represent information about the whole working mode divided by key process variables, the mean-value partial least squares model is established and is compared with the time-slice model. Then, considering that wind tunnel systems exhibit strong nonlinear characteristics, the kernel partial least squares method, which is suitable for nonlinear systems, is used to predict the Mach number. The results show that the mean-value model is better than the time-slice model, the models for single modes show better prediction abilities than those for multiple modes, and the kernel partial least square method is more suitable for wind tunnel systems than the partial least square method.

Adapted Time Slice Model of Pinch Analysis for Direct-Indirect Heat Recovery in Buildings

Heat integration techniques such as pinch analysis can play a significant role in saving energy in the design of buildings. The application of pinch analysis in this sector encounters difficulties due to the highly time-dependent behavior of energy streams such as waste heat and solar thermal collectors, as well as the possible need for heat storage units (HSUs). The existing pinch models in the literature either bear little relation to reality because they ignore the time dependency of the streams, or they do not respect the pinch analysis minimum temperature difference of the system, which leads to temperature penalties. This study introduces a novel and straightforward adapted time slice model of pinch analysis, beneficial for energy targeting in buildings. First, an algorithm for the selection of the appropriate time slice duration is proposed. Then, additional steps are embedded in the conventional problem table algorithm to account for both direct heat transfer (co-existing streams) and indirect heat transfer (time mismatched streams requiring thermal energy storage). i.e., the modified table includes both external and internal streams, respectively. The detailed application of the proposed model is demonstrated through the analysis of a direct-indirect heat recovery system for a residential test building equipped with waste heat and solar energy, considering a summer’s day. This case study, or sample calculation, determines the HSU specifications, including their design temperatures and volumes. A heat exchanger evaluation quantifies both their number and their thermal conductances, which are an economic indicator of the system capital cost.

Total Site Heat Integration benefiting from geothermal energy for heating and cooling implementations

Geothermal energy is a promising renewable energy source that has been developed by many countries in recent years. It can be utilised to meet various energy demand. This paper studies the performance of integrating geothermal energy in the Locally Integrated Energy Sector (LIES). The heating and cooling demand of various processes should be satisfied, and heat among processes should be recovered. This is done by using Grand Composite Curves and Total Site Profiles to visually illustrate how much load is required for utility systems. The geothermal utility system and steam utility system are compared. The integration plan for geothermal energy under different temperatures are studied. An illustrative case shows that by using this type of renewable energy under a specific and favourable condition, above 70% of steam utility load can be saved. The working cycle of using a geothermal utility system is studied by using the Time Slice model. The heat recovery plan for normal operation, mineral scaling, and cleaning periods are optimised. The minimum temperature for heat storage can also be identified.

A systematic pinch approach to integrate stratified thermal energy storage in buildings

In this study, dynamic streams including space heating, domestic hot water, renewable solar thermal energy and waste heat such as gray water are extracted from a typical multi-family building under extreme winter conditions for one day or 24 h. The maximum heat recovery (MHR) is targeted through an adapted time slice model (TSM) of pinch analysis. Time slices are selected such that all changes in dynamic heat capacity flow rates can be included in the pinch analysis. Moreover, charging and discharging streams of the thermal energy storage (TES) is calculated based on the adapted TSM graphical approach. The integration of a heat pumping system, gray water and solar thermal collectors through mixed direct/indirect heat recovery (i.e. via TES) can reduce hot utility usage in the studied case by as much as 72 percent. An appropriate heat exchanger (HE) and TES network is proposed for the test building to benefit from this underutilized resource. Additionally, the dynamic thermal behavior of the proposed stratified TES is numerically investigated. The results reveals that combined heat loss and thermocline thickness can reduce the heat recovery from TES tank by 10 percent, which is 2 percent of MHR in the test building.

Multi-period energy targeting for Total Site and Locally Integrated Energy Sectors with cascade Pinch Analysis

Total Site (TS) analysis for incorporating short-term or daily energy variation has been introduced in the previous studies as an extension of the Time Slice Model for the Heat Integration of batch processes. However, the energy supply and demand fluctuation could also be affected by changing customer demands due to seasonal climate variations, economic downturn, maintenance, plant turn-around, plant operability issues and raw material availability. This paper extended the cascade energy targeting methodology for TSHI incorporating long- and short-term heat energy supply and demand variation problem. The methodology aims to estimate the energy requirements of the TS system considering seasonal energy storage system as a feasibility study for energy efficiency project. A newly extended algebraic tool, known as Seasonal Total Site Heat Storage Cascade (Seasonal TS-HSC), is introduced in the methodology for modelling the energy flow between process units and storage facilities. The general tool could be used for different storage systems. This proposed tool includes the estimation of energy losses through self-discharge, charge and discharge process based on the energy storage system performance. The methodology is illustrated by a case study, which integrates batch processes, community buildings and space heating system. Implementation of the developed methodology on the case study resulted in 93.4% (low-pressure steam - LPS) and 38.2% (hot water - HW) heating requirement reduction via seasonal energy storage system application at two utility levels. The result shows the energy requirement reduction, which contributes to profitability margin improvement, greenhouse gas emission reduction potential and regional sustainability enhancement, through seasonal energy storage system in the industrial energy system.

Integration of a solar thermal system in a medium-sized brewery using pinch analysis: Methodology and case study

In the food industry a major portion of thermal energy is required for low temperature applications (below 100°C). As a consequence, there is a significant potential to substitute fossil fuels by the use of solar heat. This paper presents a methodology that first uses pinch analysis to optimize a medium-sized Scottish brewery from a direct heat recovery perspective followed by the integration of a solar thermal system. Both the time average model and time slice model were used to determine direct and indirect heat recovery potentials. In a second stage, an optimization of a chosen integration point was conducted to assess the viability of the resulting design concept. The economic analysis includes an assessment of the impact of restrictions in the UK government’s Renewable Heat Incentive program. It was determined that since solar thermal systems are financially supported up to an installed capacity of 200kW, solar heat can only account for a maximum of 7.7% of the heat demand based on the investigated brewery. However, if there was no limitation in capacity, from an economic point of view, the solar fraction could almost be doubled drawing into question the need for the restriction. Nevertheless, a CO2 saving potential of approximately 38tons per year in conjunction with a payback period of 6.4years was determined.

Estimating Freeway Travel Times Using the General Motors Model

Travel time is a key transportation performance measure because of its diverse applications. Various modeling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a microsimulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macroscale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in Saint Louis, Missouri, were used to verify the estimated travel times produced by the proposed General Motors travel time estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times.

Design and analysis of heat recovery system in bioprocess plant

The paper deals with the heat integration of a bioprocess plant which produces yeast and ethyl-alcohol. The referent plant is considered to be a multiproduct batch plant which operates in a semi-continuous mode. The design of a heat recovery system is performed by batch pinch analysis and by the use of the Time slice model. The results obtained by direct and indirect heat integration approaches are presented in the form of cost-optimal heat exchanger networks and evaluated by different thermodynamic and economic indicators. They signify that the heat recovery system without a heat storage opportunity can be considered to be a more profitable solution for the energy efficiency increase in a plant.

Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

Overhead-aware Load Distribution and System Shutdown for Energy-Efficient Computing

The energy consumption of server farms is steadily increasing. This is mainly due to an increasing number of servers which are often underutilized most of the time. In this paper we discuss various strategies to improve the energy efficiency of a datacenter measured by the average number of operations executed per Joule. We assume a collection of heterogeneous server nodes that are characterized by their SPECpower-benchmarks. If a time-variable divisible (work)load should to be executed on such a datacenter the energy efficiency can be improved by a smart decomposition of this load into appropriate chunks. In the paper we discuss a sophisticated load distribution strategy and extend it by an adaptive power management for dynamically switching underutilized servers to performance states with lower energy consumption. Of course, also transitions to higher performance/energy states are possible if required by the current load. We introduce a new time slice model that allows a reduction of the switching overhead by means of a few merge and adjust cycles. The resulting ALD+ strategy was evaluated in a webserver environment with real Wikipedia traces. It achieved significant reductions of the energy consumption by the combination of load distribution and server switching by means of the time slice model. Moreover, ALD+ can be easily integrated into any parallel webserver setup.

Heat exchanger network synthesis for batch processes by involving heat storages with cost targets

Based on time slice model (TSM), two methods, in both of which heat storages are employed to realize the indirect heat exchange operation between different time intervals, are proposed for the synthesis of heat exchanger networks (HENs) for batch processes. Firstly in the first method, a non-linear programming (NLP) model aiming at the minimum total annual cost (TAC) of networks is formulated, with which the operating cost for utilities as well as the capital cost for heat exchanger units are minimized simultaneously. Direct and indirect heat exchange operations are both considered, so this method is suitable for the two time intervals in which cold and hot streams coexist. However, when there are time intervals in which only either cold or hot streams involved, the first method is not feasible to fix the problem. So the second method is proposed in which only indirect heat exchange operation is considered. A graphical approach is introduced for the sub-networks synthesis of concerned intervals. The proposed two methods are complementary as they are applicable to different interval cases. Temperatures of heat storages in both methods are all determined through building mathematical models with the target of minimum TAC. Heat storages are added between the different time intervals and more heat can be recovered with the TAC reducing in some extent. The better HEN structure is gotten and the problem on matching streams of different time intervals is solved through the proposed methods. At last, the two methods are combined to solve an example to demonstrate the application and effectiveness of the proposed methods.

Heat integration in batch processes including heat streams with time dependent temperature progression

Time Slice Model (TSM) is an existing model suitable to identify the potential for heat integration in batch processes. However, heat streams occurring during a heating or cooling process in a batch vessel cannot be depicted rigorously due to their time dependent temperature profile. Yet such streams can frequently be found in batch processes. Ignoring the temporal dependence of the temperature can lead to inaccurate heat integration targets. In this publication, necessity for the inclusion of time dependent heat streams is illustrated and drawbacks of existing approaches are demonstrated. A methodology for considering heat streams with temperature profiles in TSM is presented and it is demonstrated that introducing additional time slices allow prediction of integration potential without accuracy loss.

Indirect thermal integration for batch processes

Rigorous algorithms to target the minimum utility requirements for single as well as cyclic batch processes are proposed in this paper. Practically, heat integration between two different time intervals requires indirect integration through intermediate fluid. Targets, calculated via proposed methodology, account for indirect thermal integration in batch process. The proposed methodology overcomes limitations of existing methodologies and guarantees the optimality as it is proved to be optimum using rigorous mathematical arguments. This methodology is applicable to any fixed-scheduled batch process. Applicability of the proposed methodology is demonstrated through illustrative examples. In one of the illustrative examples, a reduction of 18.7% and 16.4% (in comparison to time slice model) is observed in hot and cold utility requirements, respectively.

Representing topological relationships for spatiotemporal objects

Several representations have been created to store topological information in normal spatial databases. Some work has also been done to represent topology for 3D objects, and such representations could be used to store topology for spatiotemporal objects. However, using 3D models has some disadvantages with regards to retrieving snapshots of the database. This paper aims at creating a spatiotemporal version of the sliced representation that supports efficient retrieval of snapshots of the past and that supports enforcing topological relationships. This paper aims to extend an earlier representation of moving objects so that it can also store and enforce some of the topological relationships between the objects. One use of such a representation is storing a changing spatial partition. As part of the effort to construct the model, an analysis of the topological relationships has been carried out to see which need to be stored explicitly and which can be computed from geometry. Both a basic time slice model and a 3D model are examined to determine how suitable they are for storing topological relationships. An extension of the time slice model is then proposed that solves some of the problems of the basic time slice model. Some algorithms for constructing the new model from snapshots of the objects along with an adjacency graph have been created. The paper also contains a short analysis on how to handle current time, as the time slice model is best at handling historical data, and on ways to speed up searches in a database in which objects of many types are connected to one another and many files therefore potentially need to be accessed.

Sequential Methodology for Scheduling of Heat-Integrated Batch Plants

Optimal scheduling of tasks to be performed in different unit operations is of paramount importance in the batch processes. The pressure toward sustainable operation has warranted more attentions to be paid to the issues of heat integration to reduce the utility consumption. This paper presents a new approach for incorporating heat integration in batch process scheduling. The method is based on a sequential framework, where the overall problem is decomposed into two sequentially solved problems of scheduling and heat integration. First, the schedule is optimized to meet the economic objective such as makespan or profit. Next, alternate schedules are generated through a stochastic search-based integer cut procedure that adds further constraints to the scheduling formulation. Such schedules may be the alternate optima of the scheduling problem. Finally, heat integration analysis coupled with the time average model (TAM) and time slice model (TSM) is applied to each of the resulting schedules to establish the minimum utility targets. The approach is based on the precept that opportunities for heat integration between tasks are higher in near-optimal schedules where tasks are temporally clustered together. The method differs from other sequential approaches published in the literature in that the heat integration problem is solved with the intent to retain the optimality of the scheduling solution. The proposed method has the ability to handle problems too complex to be solved using simultaneous optimization approaches. It has been applied to two literature case studies. The first one involves profit maximization as the objective function. The second one entails a more complex scenario involving makespan and utilities. The results from both case studies demonstrate the effectiveness of the approach.

Estimation of CO2 Discharge from Urban Expressway: Cell Emission Approach

This paper proposes a Cell Emission Approach for estimating CO2 emission in urban expressways. The estimation models rely on traffic flow data collected by traffic detectors. Two cell-based estimation models, called as the instantaneous model and the time slice model are proposed. The instantaneous model is one of the simplest methods for estimating CO2 emission. The time slice model is more accurate model than the instantaneous model because trajectories of each vehicle in a section of an expressway are considered. CO2 emission of the Hanshin Expressway in Japan is estimated by the proposed models. As the results of the estimation, gaps between the instantaneous model and the time slice model are confirmed. The qualitative characteristics of CO2 emission in the urban expressway section are presented by the visualization methods.

Sulphate trends in Europe: are we able to model the recent observed decrease

Abundance of sulphate in Europe has decreased substantially during the last two decades. In this paper, we investigate these recent trends in sulphate concentrations by applying the Oslo CTM2 model using three different sets of SO2 emission inventories. We perform time slice model simulations with emissions for the years 1985, 1995 and 2000 and compare our results with observations to investigate if there is consistency between measured and modelled sulphate trends. Overall the model reproduces the levels of sulphur and the decreasing sulphate trends reasonably well, although some discrepancies exist. The model shows a strong reduction in the surface concentration of sulphate similar to the observations, although a slightly smaller decrease. Continental and Eastern Europe experience the largest decrease in sulphate from 1985 to 2000; observations give 65 and 63% decrease, respectively, while modelled decreases are from 42 to 58% depending on the inventory. We have also studied to what extent our model results are sensitive and robust. Based on our model simulations we find that the EMEP emissions of the three sets of emission inventories are best to reproduce the trends in sulphate observations.

Evaluation of Speed-Based Travel Time Estimation Models

Travel time estimation models, which rely on speed data provided from point detectors (usually inductive loops), have application in travel time prediction and network performance monitoring. Unfortunately there are limited, and at times counterintuitive, results in the literature about their performance. This paper focuses on the field evaluation of four speed-based travel time estimation models, namely, the instantaneous model, the time slice model, the dynamic time slice model, and the linear model. Those models are evaluated using data from two operational motorways in Melbourne, Australia. Travel time estimation errors are quantified against actual travel times measured using a timed number plate survey and time-stamped toll tag data. There was little difference in the travel time estimation error across the models and they were all found to underestimate actual travel times. Errors ranged from about 7% in the off peak up to 15% in the peak. Marginal improvements in model performance were achieved through careful selection of which detectors provide input for each section (upstream, downstream, or the average of those values) and by conversion of the inputs from time mean speed to an estimate of space mean speed.