A design approach for multiple drive belt conveyors minimizing life cycle costs

Energy efficiency of belt conveyors has recently gained in importance worldwide. While significant research efforts were consecrated to the operational aspects, the literature study shows the design optimization problem was scarcely investigated in the past. Among the various type of belt conveyors, the multi-drive technology is now increasingly acknowledged as involving further cost saving opportunities as a result of the possible reduction of the belt weight. In this paper, a multi-drive belt conveyor sizing model that aims to minimize the life cycle cost of the conveyor is presented. The effectiveness of the proposed approach in improving their economic benefits over the single-drive conveyors has been established through extensive simulations on a practical case study. The robustness of the best design solution against the variation in the inflation rate have been also validated.The National Hub for Energy Efficiency and Demand Side Management and the National Research Foundation of South Africa under grant unique number 105047.http://www.elsevier.com/ locate/jclepro2019-11-10hj2018Electrical, Electronic and Computer Engineerin

Analysis of a Dataset for Modeling a Transport Conveyor

The analysis of the works, which considered the use of neural networks for modeling a multi-section transport conveyor, was carried out. The prospects for the use of neural networks for the design of highly efficient control systems for the flow parameters of a multi-section transport conveyor are studied. The problem that limits the use of neural networks for building control systems for the flow parameters of a multi-section transport conveyor is considered. The possibility of constructing generators for generating a data set for the process of training a neural network is being studied. A method for generating a data set based on experimentally obtained measurements of the instantaneous values of the input material flow as a result of the operation of industrial transport systems is proposed. Using dimensionless variables, a statistical analysis of a stochastic flow of material entering the input of the transport system was performed. An estimate of the correlation time of a stochastic process characterizing the input flow of material is given. The recommendations on choosing the type of correlation function for the model of the input material flow were confirmed. It is demonstrated that the input flow of material is a non-stationary stochastic process. Approximations for modeling the input flow of materials of the operating transport system are considered

Optimal Electricity Cost Minimization of a Grid-Interactive Pumped Hydro Storage Using Ground Water in a Dynamic Electricity Pricing Environment

Published ArticleThe electricity price arbitrage from the utility grid can be a major source of revenue for energy storage systems. In most countries, the electricity price is tightly regulated by their government statutory authority or energy regulator to which obey the different power utility and distribution companies. It is worthwhile analyzing whether energy storage systems, such as Pumped Hydro Storage systems (PHS) using ground water, are economically viable in such a given electricity market, and determining what the benefits are of optimally operating these systems in arbitrage environments for privately owned PHS primarily used for auto-consumption or self-sufficiency. In this paper, an optimal energy control of an 8 kW grid-interactive Pumped Hydro Storage system using ground water in a farming environment is presented. A typical small farming activity within the Mangaung municipality in Bloemfontein, South Africa, is selected as a case study. The aim is to evaluate the potential energy cost saving, achievable using the proposed system. Therefore, the two objectives are to minimize the cost of energy drawn from the utility, while maximizing the energy injected under the Time-of-Use and Feed-in-Tariff schemes. Thereafter, the performance of the developed model to maximize the proposed PHS economic profitability is analyzed through a case study simulated using Matlab. Simulation results show that a potential of 68.44% energy cost saving can be achieved using the proposed system rather than supplying the load demand by the grid exclusively. From the break-even point analysis conducted, it has been revealed that after 2.25 years corresponding to a cost of $7336, the cumulative costs were lower for the proposed system as opposed to the baseline. Furthermore, the payback analysis has shown that the system can be paid off after 5.9 years of operation

A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock

The construction industry has witnessed an increase in the use of digital tools and smart solutions, particularly in the realm of building energy automation. While realising the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, which are considered critical urban development units. To support the interplay of smart solutions between buildings and neighbourhoods, this study aimed to collect and review all the smart solutions presented in existing scientific articles, the technical literature, and realised European projects. These solutions were classified into two main sections, buildings and neighbourhoods, which were investigated through five domains: building-energy-related uses, renewable energy sources, water, waste, and open space management. The quantitative outcomes demonstrated the potential benefits of implementing smart solutions in areas ranging from buildings to neighbourhoods. Moreover, this research concluded that the true enhancement of energy conservation goes beyond the building’s energy components and can be genuinely achieved by integrating intelligent neighbourhood elements owing to their strong interdependencies. Future research should assess the effectiveness of these solutions in resource conservation

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection

Optimal sizing and operation of pumping systems to achieve energy efficiency and load shifting

This dissertation presents a pumping system operation efficiency improvement solution that includes optimal selection and control of the water pump. This solution is formulated based on the performance, operation, equipment and technology (POET) framework. The focus is on the minimization of the operational energy cost. This efficiency improvement solution is divided into three stages in accordance with the operation category of the POET framework. The first stage is to select the optimal pump capacity by considering both energy efficiency and load shifting requirements. The second stage is to develop a flexible pump controlling strategy that combines and balances the contributions from energy efficiency and load shifting. The last stage is to improve the robustness of the control system using the closed-loop model predictive control approach. An optimal pump capacity selection model is formulated. In this model, additional capacity requirements for load shifting are considered along with the traditional energy efficiency requirements. By balancing the contributions from load shifting and energy efficiency, the operational energy cost can be reduced by up to 37%. An optimal pump control is formulated. The objective of this control model is to balance the energy efficiency and load shifting contributions during the operation and minimize the operational energy cost. This control model is tested under different operational conditions and it is compared to other existing control strategies. The simulation and comparison results show that the proposed control strategy achieves the lowest operational energy cost in comparison to other strategies. This optimal pump control model is further modified into the closed-loop model predictive control format to increase the robustness of the control system under operation uncertainties. A mixed integer particle swarm optimization algorithms is employed to solve the optimization problems in this research. AFRIKAANS : Hierdie verhandeling bied ’n verbeterde oplossing vir die operasionele doeltreffendheid van pompstelsels wat die optimale keuse en beheer van die waterpomp insluit. Hierdie oplossing is geformuleer op ’n raamwerk wat werkverrigting, bedryf, toerusting en tegnologie in ag neem. Die oplossing fokus op die vermindering van bedryfsenergie koste. Hierdie oplossing is onderverdeel in drie fases soos bepaal deur die bedryfskategorie gegrond op die bogenoemde raamwerk: Die eerste fase is die keuse van die optimale pompkapasiteit deur beide energiedoeltreffendheid en lasverskuiwing in ag te neem. Die tweede fase is om ’n buigbare pompbeheer strategie te ontwikkel wat ’n goeie balans handhaaf tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing. Die derde fase is om die stabiliteit van die beheerstelsel te verbeter deur gebruik te maak van ’n geslote-lus beheermodel met voorspellende beheer (Predictive Control). ’n Model vir die keuse van optimale pompkapasiteit is geformuleer. In hierdie model word vereistes vir addisionele pompkapasiteit vir lasverskuiwing sowel as vereistes in terme tradisionele energiedoeltreffendheid in ag geneem. Deur die regte verhouding tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing te vind kan ’n besparing van tot 37% op die energiekoste verkry word. Optimale pompbeheer is geformuleer. Die doel van die beheermodel is om die bydraes van energiedoeltreffendheid en lasverskuiwing te balanseer en om die bedryfsenergie koste te minimiseer. Hierdie beheermodel is getoets onder verskillende bedryfstoestande en dit is vergelyk met ander bestaande beheerstrategiee. Die simulasie en vergelyking van resultate toon dat die voorgestelde beheerstrategie die laagste bedryfsenergie koste behaal in vergelyking met ander strategiee. Hierdie optimale pomp beheermodel is verder aangepas in ’n geslote beheermodel met voorspellende beheerformaat om die stabiliteit van die beheerstelsel te verbeter onder onsekere bedryfstoestande. ’n Gemende heelgetal partikel swerm optimisasie (Mixed interger particle swarm optimization) algoritme is gebruik om die optimiseringsprobleme op te los tydens hierdie navorsingsoefening.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer EngineeringUnrestricte

A power dispatch model for a ferrochrome plant heat recovery cogeneration system

A Organic Rankine Cycle waste heat recovery cogeneration system for heat recovery and power generation to relieve grid pressure and save energy cost for a ferrochrome smelting plant is investigated. Through the recovery and utilization of previously wasted heat from the facility’s internal smelting process off-gases, the cogeneration system is introduced to generate electrical power to supply the on-site electricity demand and feed electricity back to the utility grid when it is necessary and beneficial to do so. In addition, the cogeneration system generates cooling power through a lithium bromide-water solution absorption refrigeration cycle to meet the cooling requirements of the plant. The heat recovery process for power generation is modeled and the optimal power dispatching between the on-site loads and the utility grid is formulated as an economic power dispatching (EPD) problem, which aims to maximize the plant’s economic benefits by means of minimizing the cost of purchasing electricity from the utility and maximizing revenue from selling the generated electricity to the grid. Application of the developed model to a ferrochrome smelting plant in South Africa is presented as a case study. It is found that, for the studied case, more than $1,290,000 annual savings can be obtained as a result of the proposed heat recovery power generation system and the associated EPD model. In addition to this, more than$920,000 annual savings is obtained as a result of the generated cooling power via the proposed absorption refrigeration system. The combined cogeneration system is able to generate up to 4.4 MW electrical power and 11.3 MW cooling power from the recovered thermal energy that was previously wasted.http://www.elsevier.com/locate/apenergy2019-10-01hj2017Electrical, Electronic and Computer Engineerin