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With the increasing popularity of renewable energy sources and the globally increasing electricity demand, the task of balancing the intermittent energy supply with varying demand becomes increasingly difficult. Instead of adjusting the supply, improving the demand response (DR) can be a more efficient way to optimize power balance. HVAC (heating, ventilation, and air-conditioning) systems, which operate on the demand side of power-grids, have a huge potential to improve the power balance. To assess their potential in a variable air volume (VAV) air-conditioning system with energy storage tank we introduce a demand response method that combines active cool-energy storage (ACES) with global temperature adjustment (GTA). To confirm the effectiveness of this combined ACES+GTA approach, we conduct measurements with the help of a full-scale VAV air-conditioning test setup. The experimental results are compared with a TRNSYS simulation. The measurements indicate that an energy-storing water-tank can effectively reduce the number of starts and stops for the heat pump. The simulation confirms that the ACES+GTA method can also effectively reduce the peak load of the power grid with little impact on the thermal comfort of the energy consumers. The cost-saving rate, compared to the conventional operating mode (no energy-storage during other periods), reaches 7.02% for an entire cooling season if the GTA method (with DR) is used.

Due to the excessive amount of exhaust gas emitted by its activities, the seaport is considered to be one of the sources of the deterioration of the marine environment. In addition, most seaports rely on the National Grid as an energy source to meet domestic and ship power needs. However, nowadays, the maritime industry requires an environmentally-friendly operation of seaports. This paper presents a summary discussion of the concept of green ports and some of their aspects. It further offers a bird-eye view of some of their applications worldwide. Meanwhile, an initiative is made to attempt applying renewable energy power sources to a selected case-study; “Port of Jabel Ali”, to examine the feasibility of applying such aspects of the green port concept to this port. A prototype experiment is setup to assess the wind and solar potential in the region, aided with a simulation model using HOMER™ software, to verify the experimental results and to confirm the principle. Results show that the three selected cases show promising performances while applying the renewable energy application through the maritime port with the main grid. When the solar PV system is applied to the system, a total cost reduction of 10% is achieved as well as a 10.5% reduction in levelized cost of energy. When the wind farm system is applied, 20.8% cost reduction with corresponding 21% lower levelized cost of energy is achieved. Therefore, the wind farm and solar PV system achieve 28.7% cost reduction and 31.8% reduction of the levelized cost of energy.

We develop a novel approach for quantitatively analysing future storylines of change by combining econometric analysis and Monte Carlo simulation for four different storylines of change in the EU's energy innovation system. We explore impacts on three key innovation outcomes: patenting (innovation), co-invention (collaboration), and technology cost reduction (diffusion). We find that diverse mixes of policy instruments stimulate collaborative innovation activity. We find that both RD&D expenditure and trade imports support knowledge generation and exchange, and that these relationships are largely robust to future uncertainty. Conversely, we find that policy durability and stability are only weakly linked to innovation outcomes, suggesting that adaptive policy responding to rapidly changing innovation environments should play an important part of the EU's energy future. Keywords: Monte Carlo simulation, Econometric analysis, Scenario analysis, Energy technology innovation system

Currently a considerable and the most economically justified potential of making the solar energy conversion more efficient and making the solar energy more attractive for consumers involves the coordination of the entire equipment from generation to consumption while considering all this equipment as a unified system and taking the degree of satisfaction of the consumer needs as the measure of efficiency. A photovoltaic water pumping system (PVWPS) is the first and one of few types of ground photovoltaic systems where the consumption equipment was always considered from the onset as part of the system. So a retrospective analysis of PVWPS research is of particular interest. This article contains the PVWPS research analysis beginning with the first research conducted in 1964–1966 to date. Discussed herein are the main problems that arose in the PVWPS development, operation, and optimization as well as options of their solution. Various areas of PVWPS improvement research were analyzed, including research involving photovoltaic array structure and photovoltaic modules interconnection; development and improvement of special structures of any components (pumps and invertors, etc.); improvement of photovoltaic array and electric motor interaction, electric motor and pump interaction against the backdrop of irradiance variation and in conjunction with consumption optimization and control improvement, to start with maximum power point tracing. It would make sense to aim further research involving cost reduction, productivity gain, and PVWPS service life extension at the expansion of the fields of PVWPS application, improvement of individual PVWPS components, and a more efficient use of water that is drawn. One of the prospective lines of PVWPS improvement and upgrading is to include them in the system of end user consumption equipment and assess the system efficiency not at the pump output and not in the point of end consumption equipment but after the water consumption. Studying PVWPS with the account of the fact that, in real conditions, the average output power of photovoltaic array depends not only on irradiance but also on the combined effect of all environmental conditions (climatic parameters, current state of atmosphere and air, surrounding objects, etc.) is of great importance, today.

International firms’ activities and structures have become highly complex as a result of managing a vast network of entities located around the world. Complex technology such as Additive Manufacturing or generally accepted as 3D printing has received universal interest. Economic growth in Vietnam generates unique infrastructure, logistics services and transportation challenges and opportunities. Logistics are a critically important priority in international trade, foremost in ongoing regional economic expansion. The main objective of this paper is to examine the impact and benefits of advanced technology, affecting logistics services and transportation in Vietnam. This research applied a descriptive survey methodology to gather primary empirical data on the awareness and the willingness to adapt to the technology of organizations focusing on opportunities for the Vietnam market. The results indicated that organizations have a positive attitude towards 3DP impact. The stronger influence was discovered on customization, new customer value proposition, the competitive advantage, and transportation and storage cost reduction. Vietnam is currently one of the most attractive emerging markets in the world. The findings from this research provide timely and valuable new insights into this phenomenon, as a potential mechanism for assisting the revolution of the transportation industry towards improving traffic congestion and pollution.

The use of retired batteries from electric vehicles as a second-life battery energy storage system has been recognized as a way to break the high investment cost limitation of battery energy storage systems with the associated cost reduction of a park-level integrated energy system. The battery degradation and replacements should be further considered in the planning period, especially compared with new battery energy storage systems. Additionally, there is a lack of discussion on utilizing thermal energy storage systems in coordination with second-life battery to reduce degradation. For this reason, an electric/thermal hybrid energy storage system planning method for park-level integrated energy systems with second-life battery utilization is proposed. A cumulative battery life loss calculation model is developed. A bi-level optimal planning model of electric/thermal hybrid energy storage system using second-life batteries, including an upper-level planning model and a lower-level operating model, is proposed. At the upper level, to maximize the net present value during the planning stage, the capacity of the hybrid energy storage system as well as when the second-life battery energy storage system needs to be replaced are optimized. At the lower level, the operation schemes are optimized to obtain the minimum annual operating cost, which are fed back to the upper level. The proposed method considers continuous capacity degradation of second-life batteries and mutually beneficial relationships between thermal energy storage and second-life batteries. Finally, a case study demonstrates the economic effectiveness and battery service life improvement of the proposed planning method.

[EN] The rapid progress of modern AI tools for automatic speech recognition and machine translation is leading to a progressive cost reduction to produce publishable subtitles for educational videos in multiple languages. Similarly, text-to-speech technology is experiencing large improvements in terms of quality, flexibility and capabilities. In particular, state-of-the-art systems are now capable of seamlessly dealing with multiple languages and speakers in an integrated manner, thus enabling lecturer¿s voice cloning in languages she/he might not even speak. This work is to report the experience gained on using such systems at the Universitat Politècnica de València (UPV), mainly as a guidance for other educational organizations willing to conduct similar studies. It builds on previous work on the UPV¿s main repository of educational videos, MediaUPV, to produce multilingual subtitles at scale and low cost. Here, a detailed account is given on how this work has been extended to also allow for massive machine dubbing of MediaUPV. This includes collecting 59 h of clean speech data from UPV¿s academic staff, and extending our production pipeline of subtitles with a state-of-the-art multilingual and multi-speaker text-to-speech system trained from the collected data. Our main result comes from an extensive, subjective evaluation of this system by lecturers contributing to data collection. In brief, it is shown that text-to-speech technology is not only mature enough for its application to MediaUPV, but also needed as soon as possible by students to improve its accessibility and bridge language barriers., We wish first to thank all UPV lecturers who made this study possi-ble. We are also very grateful for the funding support received by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 761758 (X5gon) , the Spanish government under grant RTI2018-094879-B-I00 (Multisub, MCIU/AEI/FEDER) , and the Universitat Politecnica de Valencia's, Spain PAID-01-17 R&D sup-port programme. Funding for open access charge: CRUE-Universitat Politecnica de Valencia

Interest from industry and policy makers on the use of autonomous goods vehicles (AGVs) for commercial use, has increased rapidly as the underlying technology has developed. This study looks at the potential impact of autonomous road haulage on logistics operations and barriers for commercial implementation in the UK. The research focuses on large goods vehicles, capable of automated driving, where the driver is not required for some or all of the journey, for logistics operations between suppliers, manufacturing plant, and retailer. The data is collected through semi-structured interviews and scoring questionnaires. All 76 participants had experience working in the logistics industry, from various parts of the operations process. The data was analysed using thematic analysis followed with the results subsequently ranked by participants to identify the importance and perceived impact. Results revealed a near-term barrier for adoption of autonomous technology, is the lack of standardisation, as technology is progressing in many different directions, with little consultation on what features are needed or how they will be implemented. The findings show the main impact of commercialisation of autonomous road haulage are overall maintenance cost reduction, due to less wear and tear, and operational cost reduction, especially for long-haul journeys, as vehicles can operate for longer hours. Moreover, despite barriers in the short term, such as public acceptance and transition costs, these will likely be outweighed by improvements in efficiency and utilisation. Based on findings from the study, we present six key considerations for the introduction of commercial autonomous road haulage, which can be used as a guide for policy makers, insurance companies, transport planning authorities, and vehicle manufacturers.

In electricity sector is possible to collect large quantities of data that contain information on relevant processes and events that occur in a given period. It gives a knowledge of the different operation conditions of the electrical network and its components. Through the treatment and analysis of these data is possible to propose market, cost reduction, reduction of failures and repairs in machines and inventory decrease strategies. Grid operator can implement strategies to improve indicators of reliability and quality of service. From a maintenance point of view, the equipment operating time is a relevant aspect to identify and solve failures without service suspensions. This paper aims to show distribution transformers failures characteristics data using historical data collected by the grid operator (Compania Energetica de Occidente) at Cauca Department (Colombia), under the cooperation of the Universidad del Cauca and Universidad del Valle. The dataset could be helpful to researchers and data scientists who use machine learning to develop applications that help engineers in predictive maintenance.

This study simultaneously explores the effects of “consolidation” and “privatization” of ports that are located in proximity to one another. The main benefit of consolidation is assumed to be economies of scale due to the aggregation of container cargo in one port, while that of privatization is cost reduction for port management and operations. We employ a multi-agent simulation model to express interactions among stakeholders, such as a port management body, shipping company, and shipper. The model is applied to a case study for the Kobe and Osaka ports in Japan. We find that consolidation has a larger impact than privatization in terms of cargo volume and total surplus. In particular, if future container cargo increases, consolidation accelerates the increase in cargo volume of the Kobe and Osaka ports. Transshipment cargo receives the largest volume in the case of “consolidation and privatization” when future container demand is increasing. However, the Busan port in South Korea, competitor of the Kobe and Osaka ports for transshipment cargo, would remain dominant even after the consolidation and privatization of the Kobe and Osaka ports, since its vessel frequency would be high in all cases. Keywords: Ports in proximity, Privatization, Consolidation, Multi-agent simulation, Kobe, Osaka

This paper develops a model which can be used to minimize the electricity cost of South African farms through the usage of a wind pump with pico hydro generator and a borehole in pumped hydro storage configuration referred to as “hydro aeropower”. This model will optimally schedule the generation power flow from the pico turbine given the demand, the state of water level in the reservoir as well as the electricity pricing period. This model can be implemented to control the power flow in small farming activities where boreholes and windmill are available. Therefore, a 2kW hydro aeropower using groundwater in a farming environment and operating under the Time of Use in South Africa is used to evaluate the possible cost reduction linked to the electricity usage achievable compared to the grid.Therefore, the problem can be seen as a minimization of the cost of energy purchased from the main electricity supplier, and the maximization of the energy used by the load directly supplied by the hydro aeropower in a dynamic pricing environment. Afterwards, the techno-economic performance of the proposed system is analysed using Matlab software. The results have revealed that up to 63.62% can be saved on the cost of energy consumed using the developed setup rather than supplying the load demand by the grid exclusively. Keywords: cost reduction, Dynamic pricing, Hydro aeropower, Optimal scheduling, Pumped hydro storage

Concrete bridges are used for both highways and railways roads. They are characterized by their durability, rigidity, economy and beauty. Concrete bridges have many types such as simply supported girder bridges, arch bridges and rigid frame bridges. However, for very large spans, arch bridges are more economic in addition to their beauty appearance. In this research, a geometrical structural optimization study for a deck concrete arch bridges using Genetic Algorithms technique is presented. This research aims mainly to demonstrate a methodology to find the least cost design, in term of material volume, by finding the optimal profile. A Finite Element numerical model is used to represent the arch structure. The MATLAB programing platform is used to develop codes for Genetic Algorithms optimization technique and Finite Element analysis method. The resulted design from the optimization process is compared to traditional design and an obvious cost reduction is obtained. Keywords: Optimization, Genetic Algorithms, Finite Element, Arch bridges

Several researchers benefited from the EU supported project Sus-tainable Process Integration Laboratory - SPIL funded as project No. CZ.02.1.01/0.0/0.0/15_003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL project.This work was also partly supported by the Ministerio de Ciencia e Innovacion (Espana) /FEDER under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 and A-TIC-080-UGR18 projects., The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans., European Commission CZ.02.1.01/0.0/0.0/15_003/0000456, Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research, based on the SPIL project, Ministerio de Ciencia e Innovacion (Espana) /FEDER RTI2018-098913-B100, Junta de Andalucia, European Commission CV20-45250 A-TIC-080-UGR18

Adverse effects of conventional fossil fuel based power generation create an urge for clean technologies. Technologies related to the advancement of renewable energy and clean technologies are growing very fast. Development in renewable energy technologies, reliability and environmental restrictions generates the requirement of a Hybrid Power System (HPS). Cost is an essential factor which plays important role while planning any system. This factor can be included in planning an HPS via Pinch Analysis through the concept of prioritization. This concept is a well-established technique in Process Integration for designing cost-optimal resource conservation networks for various resources such as thermal utilities, water and carbon. However, its application towards the cost-optimal planning of an HPS is still to be explored. In this paper, a methodology is developed to set cost optimal targets in HPS. The proposed methodology extends the concept of prioritization towards planning of cost-optimal HPS. The capital cost of power generation depends on the type of power resource. The proposed method incorporates entry and exit on the time horizon of each power resource. The methodology presented in this paper calculates the power rating of power resources in an HPS required for satisfying the power demands with an objective to minimize the overall cost of electricity. The methodology is illustrated via two examples where 7.75% and 8.8% cost reduction potential are estimated.

Lifecycle cost estimation is crucial for high-value manufacturing sectors, in particular at the early product design stage, to maintain their product affordability and manufacturing profitability within the market. Accordingly, it is important to identify through-life cost reduction opportunities. However, this is a challenging task for designers at the early product lifecycle stage due to the lack of complete historical data and the existence of high-level uncertainties within the product and service cost data. Moreover, the complexity of maintenance, repair, and overhaul interventions during the operation stage reduces the designers’ decision-making confidence level at the earlier stages. This paper aims to address these challenges by proposing a novel Digital Twin (DT) architecture that uses adaptive data structure and ontologies to automatically produce the cost model from data mined information throughout a product lifecycle. The DT architecture supports designers by capturing data in terms of consumed and caused cost and automates the data flow to provide an adaptive cost estimation method across the product lifecycle. The DT enables designers to estimate the lifecycle cost at the early stage and to identify the through-life cost reduction opportunities effectively. Thereby, it is expected that the proposed DT supports OEMs to reduce the total lifecycle cost and improve the efficiency of their product development. A case study of lifecycle cost estimation in the machine tool industry is considered for testing the validity of the DT architecture.

The constantly developing power markets have been attracting the interest of the academic society for the last decades. The majority of such research focuses on the overall cost reduction of the market’s operation and the improvement of social welfare. However, the present research focuses on the electricity provider’s side and the minimization of its cost while satisfying the demand of the consumers. We propose an optimization-based methodological framework for the optimal economic portfolio management of a provider. The latter has the ability to produce electricity from its natural gas powerplant, buying from the day ahead spot market or curtailing part of the demand through demand response programs. More specifically through the development of a mixed integer linear programming (MILP) model, it quantifies the proposed new alterations of the spot market products as well as considering the consumer behavior in demand response programs. The model incorporates the ability of the provider to meet the demand via three different options as mentioned above. As far as the spot market products are concerned, it integrates the abilities of: base load contract (BLC), three kinds of peak load contracts (12,4,2-h PLCs), and standard hourly products. The last option of demand response refers to load curtailment, with an upper constraint on the curtailed power reflecting the consumer’s behavior in terms of participation, response, and persistence. The usefulness of such research is being presented by comparing the performance of three different models and achieving a significant further cost minimization of 9% in the provider’s total cost minimization.

This chapter explores techno-economic aspects of several routes on microalgae processing for biofuel, chemicals, pharmaceuticals, and bioplastics. Microalgae as biofuels feedstock offer several advantages to the earlier generation, including a large range of conversion pathways, a more efficient energy conversion process, less conflict of food versus fuel, and potentially contributing to waste management and pollution control as there is also chance to cultivate the algae in wastewater. Chemical products assessed in this chapter are the production of PUFA (lipid route), RuBisCo, and pyrrolidones (protein route). The economic valuation was mostly based on the best-case scenario, yet the analysis reveals that the production of the chemicals remain not economically feasible as the production cost is too high. Microalgae also show excellent potential as bioplastic resources and biopharmaceuticals, however, the technological options that are economically profitable have not been thoroughly investigated. An integrated production system of microalgal-based biofuels and other products, along with waste management, are the most cost-efficient. The design allows capital and operating costs borne to cover several target products via the selection of a particular technical route. Potential cost reduction is mainly from technological options, e.g., low-cost engineering equipment and maximizing extraction. An integrated production system of microalgal-based biofuels and other value-added products is promising as the capital, and operating costs are borne to cover several target products at the same time.

Direct hydrazine fuel cells (DHFCs) are one type of direct liquid fuel cells (DLFCs), where hydrazine is used as a fuel. DHFCs can be operated using a non-precious metal catalyst, which potentially contributes to cost reduction of the hydrazine fuel cell technology. Despite the tremendous progress made in the development of an active catalyst for DHFCs, however, available perfect catalyst able to meet the power output requirements for commercial fuel cell vehicles is still an issue. In this chapter, we summarize the recent advances in the development of catalysts for direct hydrazine fuel cells (DHFCs). The challenges and perspectives in this field are also discussed.

The invention of additive manufacturing (AM) has made technology development interdisciplinary by drawing various fields of knowledge from science and engineering into one technology domain to achieve optimum practical results. In the last three decades, the industrial production environment has changed from no tooling, assembly lines, or supply chains into the classic riotous technology of three-dimensional (3D) printing. The real power of 3D printing lies in the ability to derive new opportunities, products, and services that will change the effectiveness of the products developed in a more significant way. AM plays a crucial role in the emerging Industrial Revolution 4.0 (IR 4.0) for development products in the defense, aerospace, marine, automobile, and other essential mechanical engineering industries for advanced multifunctional and multimaterial systems. AM, or 3D printing, can completely revolutionize the modern manufacturing industry with a considerable cost reduction. AM technology can fabricate highly customized 3D objects where both shape and composition can be tailored along with the material. Here, we try to educate readers on AM’s advantages over traditional methods in saving time and cost. Compared to previous IR technologies, advancements in computing functions and less mass storage costs in AM for IR 4.0 have made extensive data processing of many data less complicated and effortless for modern 3D computer-aided design models with comparatively less time frame.

Agriculture is a very demanding sector, in terms of physical condition, and not all people are able to tackle it, in a satisfactory way. The problems become more apparent in case of elder people or people with disabilities. The recent technological advances and the cost reduction in the smartphone industry products and the derived systems can make this problem less intense. This work describes educationally meaningful methods to develop programs that exploit voice recognition engines, based on cloud services, so that a smartphone device can remotely trigger typical farming actions or query the values of several critical parameters of the farm. A step beyond that, the paper also assesses the case when there is no presence of Internet connection to provide access to a remote speech or voice recognition server. In this case, locally running software tackles the voice recognition issues, as embedded boards are now powerful enough to sustain such tasks. In both cases, the node can “speak” reporting the status of a specific element by using speech synthesis methods or by playing prerecorded messages. Not only Wi-Fi radios but also LoRa interfaces are adopted to carry the necessary information at longer distances matching the farm's dimensions. Finally, the paper concludes with fruitful comments on the performance of the presented work and also highlights the priorities for the future.

The recent years saw a remarkable progress in solar cell materials and designs. Among all the available photovoltaic technologies, two of them stand out from the rest because of very rapid efficiency growth: III–V semiconductors and perovskite-based solar cells (PSC). In this chapter, we review the recent history and the ultimate concepts that concern the materials application at device level. PSC were first described in 2009 and their reported efficiency quickly reached more than 24%. This unmatched efficiency evolution is accompanied by a potential cost reduction due to the ease of fabrication from solution processing. Also, the possibility of low temperature processing allows the use of flexible substrates that could include new markets that are unthinkable for other technologies. A global widespread of the PSC research helps understanding of the degradation and hysteresis issues that will in turn allow a near future commercial product. Respect to III–V materials, we witnessed the transition from the lattice matched structures to concepts that overcome the limits of the standard triple-junction cells. Graded lattice materials, named metamorphic (MM), allow bandgap engineering to optimize the solar spectrum match. Another concept explored to exceed the limits of lattice matched materials was the bonding of structures grown on different substrates. This conducted to the recent record obtained at module level in concentrating photovoltaics applications. Finally, design based on six junctions inverted MM achieved the last efficiency record of 47.1% at 143 suns. The cases considered in this chapter show two convergent strategies to improve the solar cell performance as an energy source: the potential of low cost at fairly competitive efficiencies for PSCs; and the high cell cost afforded by ultra-high efficiency of III–Vs under concentration. These emergent technologies are summoned as new actors in a world dominated by the well-established crystalline silicon technology.

The ubiquitous and least concerned pain assessment and its associated complications are treated to be prime significant and costly pain assessment. Through the inception of the Medical Internet of Things (MIoT), utilizing smart devices, the medical healthcare industry is moving toward the in-house environment, which leads to creating opportunities for the development of explanations that are effective along with significant potential in cost reduction and time saving. This chapter primarily focuses on potential applications of MIoT for capturing and classifying pain intensity levels among patients. The methodology includes reviewing the recent literature and predicting a framework that measures the pain intensity level. The MIoT has managed to open digital transformation, especially in home-based pain assessment, as it ensures personalized care along with patient engagement. The pain intensity so achieved using a multidisciplinary approach is unique for smart management of noncommunicable pain through individual and comprehensive feedback involving data mining and telecommunication, which are tailored to individual requirements. Voice along with the facial expressions plays a significant role when integrated with MIoT to accurately assess the pain level and thus opening up new opportunities in healthcare. The MIoT has brought about a revolutionary change in the healthcare sector either it is related to remote monitoring or designing smart sensors with the amalgamation of the medical gadget. As it is in its inception phase of development, it may augment pain intensity more accurately.

Concern for the environment, combined with cost reduction and efficiency improvements in renewable energy generation facilities, is leading to a significant increase in their share of the energy mix. Given the unmanageable and seasonal nature of these technologies, as well as the rules of wholesale market, large price differences appear, even over the course of the same day. In order to develop a potential business model based on energy arbitrage, a Linear Mixed-Integrated Programming model has been developed to obtain optimal strategies for buying and selling electricity by energy storage systems considering a full year of hourly electricity prices. For each configuration (battery size/inverter size), the model provides an optimal business strategy. Given the size of each yearly model and since we are interested in obtaining the best strategy for each battery-converter combination until the actual capacity of the battery is small enough to get profitable results, a sequential year-by-year solution framework has been implemented. By applying this resolution scheme, the optimization procedure provides the optimal operation strategy for each configuration. This strategy is then used to compute some financial indicators in order to select the optimal configuration. The optimization procedure is applied to the German wholesale electricity market in 2019, obtaining the optimal operation policy, as well as guidance to the research centers on the impact that the different improvements to the storage systems would have.

The vulnerability is defined as the weakness in the software system in terms of safety. In detail, the vulnerability is arisen from software failure resulted from the computer software of operating system. Many open source software are embedded in the commercial software. Then, the open source software is successful in achieving the cost reduction for several companies. Almost all of the open source software are developed by using the bug tracking system. Then, several nonfixed faults are embedded in the open source software. In particular, these nonfixed faults damage the system as critical faults. A lot of software reliability growth models have been proposed in order to assess software reliability and safety. However, there is no method for vulnerability assessment based on the fault big data for open source software. In this chapter, we discuss the method of vulnerability assessment based on the deep learning in order to consider the security of OSS project. Moreover, several numerical illustrations based on actual fault big data are shown by using the method of vulnerability assessment proposed in this chapter. Finally, we show that the proposed method is helpful to assess the vulnerability of open source software.

Risk-relevant plants like Oil & Gas (O&G) or nuclear ones are subjected to strict safety regulations. Risk Assessment is mandatory for these plants, and the damage quantification is a crucial step which has to be carefully addressed. Nowadays the state of practice for consequences estimation entails the use of semi-empirical methods which permit a fast evaluation of the large number of accidental scenarios needed for a Quantitative Risk Assessment (QRA). However, in case of large and congested industrial environments like offshore platforms or equipment inside nuclear primary containment buildings, the aforementioned methods usually lead to an overestimation of the damage areas, for example because they neglect the space congestion which highly affects the accident evolution. A more accurate analysis can be performed using the Computational Fluid Dynamics (CFD), nonetheless its high computational cost represents an important drawback. In this work a CFD approach, called Source Box Accident Model (SBAM), is presented. It models high-pressure gas releases (>10 bar) in congested environments guaranteeing a good computational cost-accuracy compromise. The aim of SBAM is to permit a fast consequences estimation (evaluation of flammable/toxic areas, etc.) via CFD, in order to have a simulations time compatible with the plants design schedule. The long-term objective is to integrate the CFD contribution in a safety driven design process. SBAM is based on the splitting of the multi-physics and multiscale phenomena characterizing the accident: the initial supersonic compressible release and the successive low speed dispersion. The first one is simulated in a small domain called Source Box (SB) and the second one in the case study domain. The two simulations are coupled in a suitable way, through proper parameters which are extensively discussed. This work presents a detailed description of SBAM and two different analyses: a sensitivity study on the coupling parameters and a numerical benchmark which uses a standard CFD simulation as reference. The sensitivity analysis shows that the coupling is a crucial step of the method and the coupling parameters must be treated in the most accurate way. The numerical benchmark shows that SBAM is not introducing significant errors with respect to a standard CFD simulation and, in addition, permits a relevant simplification in the simulation setup and computational cost reduction.

The production costs of advanced biofuel options are currently higher than those of their fossil fuel equivalents. Capital Expenditures (CAPEX) for the production of liquid biofuels for road, aviation and marine transport sectors have a significant contribution to the overall production cost, together with the feedstock cost. It is, therefore, important to estimate the potential for cost reduction through R&D and experience in assembling a growing number of respective plants (i.e., from first-of-a kind (FOAK) to the Nth plant (NOAK)), which comprise a mix of established and innovative technological components. This could provide valuable information to stakeholders for the expected investment costs to meet European Commission goals in 2050. This study adopts a methodological framework based on the “learning curve theory” to estimate cost reduction as a result from the experience of technology implementation, in terms of numbers or capacity of units implemented. This work applies the learning theory as a multicomponent analysis, which requires a systematic decomposition of the entire production process to identify established and innovative technological components that can be analysed in detail using the corresponding technoeconomic data. The analysis showed that CAPEX reduction in the range of 10-25% could be expected to reach capacities corresponding to NOAK plants in 2050. To reach further CAPEX reduction of 40%, for example, would require higher cumulative annual growth rates to achieve two orders of magnitude increase of cumulative installed capacity. This corresponds to hundreds of GWs or equivalently some hundreds or thousands of large-scale plants to meet the goal of 20-25% transportation fuels consumption to be covered by advanced biofuels in 2050.

Although traditional yarn spinning methods have prevailed, new modifications have promoted automation, cost reduction, and innovative yarn effects. This chapter covers modeling and simulation approaches for yarn formation technology. New simulation approaches have made it possible to study the yarn formation process. This chapter explains a guided approach on how modeling and simulation can be utilized to characterize twist propagation during spinning and thus yarn formation. The second section is focused on how the whole spinning process can be analyzed by using simulation and modeling. A case of compact spinning with an apron is used as a case example to explore a step-by-step approach toward designing different models and analyzing them using simulation to determine which model reduces the negative pressure and thus help to save cost. This approach of process optimizations is becoming increasingly applicable in textile engineering and other fields as it has proved to be cost-effective and high degree of precision. This chapter provides fundamentals and new approach on how yarn formation can be understood through modeling and simulation.

FRCs are used as primary structures for aerospace vehicles, launch vehicles/space craft for space exploration and in the field of spots and games. As long as weight reduction and cost reduction with improved efficiency remain important design consideration, FRCs remain to stay in aerospace and sports industries. Only FRCs can achieve the desired strength-to-weight ratio without compromising on any of the specification requirements. For military aircrafts and also for spacecrafts, increase in the payload capacity, which is the most important parameter for future missions, can be achieved only through FRCs due to the design flexibility and choices. Discovery of new fibers and matrices, development of novel process techniques and novel testing methods will accelerate the use of FRCs for aerospace applications. Fiber reinforced composites (FRCs) now became inevitable and markedly persuaded in the area of sports and games due to its classical properties such as lightweight, super strength, high intensity, good toughness, superior shock absorption and user easiness. For the production of advanced FRCs for aerospace and sports applications, a fundamental understanding of the behavior of resin, reinforcement, and their combination, production technology, test methods, inspection methodologies, structural evaluations, and environmental effects are critical. The challenges in the development of FRCs for aerospace and sports applications include the selection of material and design that are capable of operating in required filed and wide variety of conditions are given in this chapter.

The increase in energy demand, including peak power demand for electricity is one of the most important aspects to be considered in the electricity sector, as it has a negative impact on the flexibility of the power supply and the power balance in the electricity networks. Usually, some distribution system operators impose an additional cost on electricity consumption during peak hours, on other hand, they reducing the electricity price during off-peak periods to encourage householders to reschedule the use of electricity consumption. The work presented in this paper aims to propose an optimal strategy for scheduling energy consumption to help householders for reducing the cost of energy, as well as for saving energy in a residential house connected to a microgrid (Power grid system, PV, and battery storage system). The results presented are obtained using a particle swarm optimization algorithm (PSO) using Matlab. Two optimization scenarios are considered and compared to a base model to prove the efficiency and performance of the proposed optimization model. The results show that the scheduling strategy for energy consumption reduces the daily operating cost by 45% and that about 22% of energy is saved in the system.

Optimal operation of generation units is crucial when looking for reduction in energy consumption and carbon emissions in multi-energy systems (i.e. multiple generation sources, energy networks and storages). This work proposes an innovative optimization approach that can be applied to energy systems composed by multiple small units for the production and conversion of electricity, heating and cooling. The optimization is conducted acting on the operation of the production units, the capacity and operation of thermal storage units and the application of demand side management to the thermal network. The optimization procedure is based on a two-level approach, combining a genetic algorithm and a linear programming approach and including a physical model of the district heating network. Multiple scenarios corresponding with typical days are considered. An application to a realistic system, which is optimized assuming an economic objective function, is performed. Results show that thermal storage installation can reduce costs of about 1.5 %, while its integration with demand-side management leads to a cost reduction up to 4 % and allows reducing the storage size.

An intelligent system is designed in this proposal to handle this crucial situation in a better way. The algorithm is developed in such a way that medical equipment are disposed of in hospitals as normal. If the hospital has a COVID-19 block the used equipment should be disposed of in a closed lid so that the spread of viruses can be reduced. The main motive of this proposal is to reduce the human resources, improve waste disposal, and make the waste reusable. In a hospital environment there is different color code that decides which equipment should be disposed of. The doctors and the nurse are the people who decide what color code is to be used for the disposal of medical equipment. The different color-coded waste bin is chosen for making reusable waste into a fertilizer for agricultural fields. To do this process the system is designed in an efficient method and cost reduction is maintained until the end of the process. Before entering the recycling process all the waste should be sanitized. After the verification process is done on the waste materials the recycling process gets started on the basis of the color code of the waste collection. A smart bin will be designed for each color code based on the usage of the medical equipment; the segregation process will be carried out in each phase. In the final phase after the recycling process it will be used as fertilizer and will be distributed to the farmers to improve crop yield. An algorithm is going to work on data verification and checking process, which will be done automatically. After the data automation process a intimation will be sent to the final testing process where it helps us to do segregation process in an efficient way.

A photo-electrochemical reactor has been gaining interest and has been seeking improvements in design, production efficiency, and cost reduction. As these improvements can make a photo-electrochemical reactor compatible with renewable fuel production devices, photo-electrochemical reactors’ design aspects are critical to reaching optimal performance through distinct and optimal reactor architectures. Thus, this study demonstrates designing factors related to single and double reactors following prior design decisions and performance indicators as these decisions play a significant role in achieving optimal design. Therefore, the results demonstrate that simplicity, functional convenience, and operational costs are the major decisions that need to be taken before the designing phase. Overall operation yield, current efficiency, energy efficiency, overall selectivity, and specific energy consumptions are performance indicators used to compare designed reactors. These decisions and indicators can provide insights into practical reactors’ designs.

The investigation of wind farms attracts lots of researchers globally although their investment cost is quite high. Wind power, as a relatively new technology, still keeps enough research space on the study of cost reduction methods. Even a small improvement in the wind farm control or wind farm optimization can lead to a large sum of money saving. Hence, many works aim to maximize the energy production or minimize the investment cost of the whole wind farms. This chapter is organized as follows: Section 21.2 introduces wind farm active power dispatch methods. Wind farm reactive power dispatch is analyzed in Section 21.3. Wind farm layout optimization problem is presented in Section 21.4. In the end, this chapter is summarized in Section 21.5.