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This paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates.

An IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs.

The proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates.

Proposed algorithm is validated with limited number of experiments.

IIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff.

To analyze the structural changes in electrical energy production sector caused by rapidly growing economy and Lithuania's international environmental commitments to the EU and international organizations.

The case study estimates the environmental‐financial aspects of cogeneration system to be implemented at the boiler house. After quantitative estimation of the electric energy demands the financial comparison of long and mid‐term environmental measures is presented.

The study responds to European strategy towards doubling the amount of cogenerated electrical power and provides financial‐environmental estimates of cogeneration installations.

Installation and maintenance of co‐generation system requires high investments, however, in a long‐term perspective it would result in a substantial environmental and financial effect.

The proposed scheme could be adjusted to the local conditions of the individual country as one of energy production options to bring about a sustainable energy future.

The analyzed quantitative assessment of national cogenerated energy potential serves as a tool for implementation of sustainable energy production in practice.

This paper aims to suggest and research a revolutionary method‐transfer of electricity in outer Space with distance of hundreds of millions kilometers by ultra‐cool plasma cables.

Methods of the plasma and electricity physic are used for research.

Theory of plasma cable transferring is offered, developed and its possibilities researched.

This method uses a high voltage electricity and plasma source (accelerator).

Offers conclusions from the research of a revolutionary new idea‐transferring electric energy in the hard vacuum of outer space wirelessly, using a plasma power cord as an electric cable (wire). He computed the macroprojects: transference of hundreds kilowatts of energy to Earth's Space Station, transferring energy to the Moon or back, transferring energy to a spaceship at distance 100 million of kilometers, the transfer energy to Mars when one is located at opposite side of the distant Sun, transfer colossal energy from one of Earth's continents to another continent (for example, between Europe – USA) wirelessly – using Earth's ionosphere as cable, using Earth as gigantic storage of electric energy, using the plasma ring as huge MagSail for moving of spaceships.

The paper provides information on a revolutionary method for the transfer of electricity in outer space.

Direct energy utilization in nine palm‐kernel oil (PKO) mills located in Southwestern Nigeria was analyzed. The mills were stratified into small, medium and large‐scale categories, based on their modes of operations and production capacities. Evaluation of energy usage was carried out in the seven readily defined unit operations namely: palm‐nut drying, palm‐nut cracking, palm‐kernel roasting, palm‐kernel crushing, PKO expression, PKO sifting and PKO bottling/pumping. PKO extraction rates in the three mill categories were evaluated. The average PKO extraction rate for small, medium and large mills were 48.45 percent, 42.68 percent and 36.24 percent, respectively. The total energy expenditure in small, medium and large‐scale PKO mills were 350.89MJ/tonne, 230.70MJ/tonne and 181.74MJ/tonne, respectively. This suggests that the unit energy requirement for PKO output decreases as mill capacity increases. The four most highly energy‐intensive operations identified were palm‐nut cracking, palm‐kernel roasting, palm‐kernel crushing and PKO expression, altogether accounting for 95.29, 92.14 and 93.65 percent of total energy used in small, medium and large‐scale mills, respectively.

Plastic waste is one of the long-standing global issues in the recent era. Unfortunately, India is one of the countries which has been affected by the mismanagement of the use of plastics. India has recorded a substantial growth in the production of plastic and is considered a country of increased consumption of plastic. Due to the absence of an appropriate waste collection and segregation process, it has created the major issue of waste management and discarded used plastic items used for packaging application. There are various plastic waste management laws and programmes that have a cascading effect on almost every sector of business. In 2016, two years after the new union government took power in New Delhi, The Ministry of Environment, Forest and Climate Change (MoEFCC) has made some improvements in rules for the collection, segregation, processing, treatment and disposal of the waste. In the pandemic era, effective plastic waste management became more important than ever. The COVID-19 pandemic has created an increased demand for single-use plastic because of pressure on the already out-of-control global plastic waste problem. It is recorded to be large, and the magnitude of this pandemic related to mismanaged plastic waste is unknown. However, understanding the changing landscape and alarming need for effective plastic waste management, the government of India has proposed certain changes to prohibit imports, handling, manufacturing and use of single-use plastics in the country. This is in line with the government's intent to phase out single-use plastic by 2022. Considering this, this chapter highlights the changes in the rules and regulations in India related to plastic waste management and its effects on various sectors of business.

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

The purpose of this paper is to provide a method for planning and controlling energy budgets for an industrial plant. The developed method aims to obtain a very high confidence of predicted electrical energy cost to include into the estimation of budget and a continuous control of energy consumption and cost.

The authors propose a methodology that refines effectiveness and efficiency of budget estimation. The method relies on a three‐stage analysis: energy consumption characterization and forecasting, energy budget formulation and energy budget control. In particular, this paper deals deeply with the second and the third stages, i.e. energy budgeting and control. The methodology has been developed on the basis of a continuous improvement philosophy and project management techniques. A discussed case study shows the potential of the methodology in order to discover energy consumption inefficiencies.

Energy budgeting and control has been implemented within a set of first and second level metrics. The first level indicators allow identifying the effect of an increase of specific consumption beyond the predicted. The second level indicators allow identifying the effect of variations of price, volume, mix or loading bands from the predicted.

In the paper climatic variations are not considered, limiting the energy drivers to those related to production volumes.

The method can be considered as a practical guide for energy budget planning and control of any industrial consumer.

A new approach to energy budgeting and control is proposed, allowing the impact of different specific consumption or production plans (volume, mix, and load bands) to be calculated.

Improvements on the energy efficiency of the induction motors bear on not only these motors but also on the whole industry as a result of preference of these types of motors. In recent projects, energy efficiency of the induction motors is approaching to 90 per cent. The first necessary condition of the efficiency improvements is an accurate estimation of energy efficiency. This study aims to estimate the energy efficiency of induction motors by using three innovative estimation methods.

Data for 307 motors were taken from three different companies and their torque, power, power factor and speed data were used. Three hybrid models were created by estimating the error of three autoregressive (AR)-based efficiency estimation models with the back-propagation artificial neural network (ANN) structure. In these proposed hybrid models, the AR models were supported with artificial neural networks to obtain a minimum estimation error. These three hybrid models were called as AR1-ANN, AR4-ANN and residual-ANN.

Without hybridization of AR models by back-propagation ANNs, the best estimation result was obtained by residual model. On the other hand, for the proposed hybrid models, the best estimation was obtained by AR1-ANN, followed by AR4-ANN and finally the residual-ANN according to ME values.

Proposed AR-ANN hybrid models relieve of longtime experiments for the energy efficiency measurement of induction motors. Furthermore, these AR-ANN models give more accurate results than the available methods in the literature. Engineering value of this research is three different issues in finding energy efficiency. The first one is minimizing of the test cost, the second one is no requirement the test equipment and the third one is not interrupting the motor. Every company that needs motors can use these estimation methods due to the advantages.

Novel three AR-ANN hybrid models for energy efficiency estimation were studied. These novel methods give better response than the other methods which were used for estimation of induction motors in the literature.

This paper aims to propose a simple, derivative-free novel method named as Nelder–Mead optimization algorithm to estimate the unknown parameters of the photovoltaic (PV) module considering the environmental conditions.

At a particular temperature and irradiation, experimental current-voltage (I-V) and power-voltage (P-V) characteristics are drawn and considered as a reference model. The PV system model with unknown model parameters is considered as the adaptive model whose unknown model parameters are to be adapted so that the simulated characteristics closely matches with the experimental characteristics. A single diode (Rsh) model with five unknown model parameters is considered here for the parameter estimation.

The key advantages of this method are that parameters are estimated considering environmental conditions. Experimental characteristics are considered for parameter estimation which gives accurate results. Parameters are estimated considering both I-V and P-V curves as most of the applications demand extraction of the actual power from the PV module.

The proposed model is compared with other three well-known models available in the literature considering various statistical errors. The results show the superiority of the proposed model with a minimum error for both I-V and P-V characteristics.