Energy Savings in Industrial Processes: A Case Study of Strategies and Tuning Procedures for PI and PID Controllers

2012 ◽  
Vol 45 (3) ◽  
pp. 607-612
Author(s):  
F.J. Gomes ◽  
F.P. Queiroz ◽  
I.F. Lopes ◽  
A.A.R. Coelho
Keyword(s):  
Energy Savings ◽  
Industrial Processes ◽  
Pid Controllers

Literal Media Ecology: Crisis in the Conditions of Production

2017 ◽  
Vol 19 (5) ◽  
pp. 486-503 ◽  
Author(s):  
Brett R. Caraway
Keyword(s):  
Political Theory ◽  
Capital Accumulation ◽  
Energy Savings ◽  
Media Ecology ◽  
Environmental Responsibility ◽  
Media Technologies ◽  
Contemporary Media ◽  
Matter Energy

This article outlines a socio-political theory appropriate for the study of the ecological repercussions of contemporary media technologies. More specifically, this approach provides a means of assessing the material impacts of media technologies and the representations of capitalist ecological crises. This approach builds on the work of ecological economists, ecosocialist scholars, and Marx’s writings on the conditions of production to argue that capitalism necessarily results in ecological destabilization. Taking Apple’s 2016 Environmental Responsibility Report as a case study, the article uses the theory to analyze Apple’s responses to ecological crises. The article asserts that Apple’s reactions are emblematic of the capitalist compulsion for increasing rates of productivity. However, unless the matter/energy savings achieved through higher rates of productivity surpass the overall increase in the flow of matter/energy in production, ecological crises will continue. Ultimately, capital accumulation ensures continued ecological destabilization.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

The impact of acid hydrolysis conditions on carbohydrate determination in lignocellulosic materials: a case study with Eucalyptus globulus bark

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ricardo Jorge Oliveira ◽  
Bruna Santos ◽  
Maria J. Mota ◽  
Susana R. Pereira ◽  
Pedro C. Branco ◽  
...  
Keyword(s):  
Acid Hydrolysis ◽  
Eucalyptus Globulus ◽  
Industrial Processes ◽  
Lignocellulosic Materials ◽  
Optimal Conditions ◽  
Hydrolysis Conditions ◽  
The Impact ◽  
Eucalyptus Bark

Abstract Lignocellulosic biomass represents a suitable feedstock for production of biofuels and bioproducts. Its chemical composition depends on many aspects (e.g. plant source, pre-processing) and it has impact on productivity of industrial bioprocesses. Numerous methodologies can be applied for biomass characterisation, with acid hydrolysis being a particularly relevant step. This study intended to assess the most suitable procedures for acid hydrolysis, taking Eucalyptus globulus bark as a case study. For that purpose, variation of temperature (90–120 °C) was evaluated over time (0–5 h), through monosaccharides and oligosaccharides contents and degradation. For glucose, the optimal conditions were 100 °C for 2.5 h, reaching a content of 48.6 wt.%. For xylose, the highest content (15.2 wt.%) was achieved at 90 °C for 2 h, or 120 °C for 0.5 h. Maximum concentrations of mannose and galactose (1.0 and 1.7 wt.%, respectively) were achieved at 90 and 100 °C (2–3.5 h) or at 120 °C (0.5–1 h). These results revealed that different hydrolysis conditions should be applied for different sugars. Using this approach, total sugar quantification in eucalyptus bark was increased by 4.3%, which would represent a 5% increase in the ethanol volume produced, considering a hypothetical bioethanol production yield. This reflects the importance of feedstock characterization on determination of economic viability of industrial processes.

scholarly journals Increased Plant Quality, Greenhouse Productivity and Energy Efficiency with Broad-Spectrum LED Systems: A Case Study for Thyme (Thymus vulgaris L.)

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 960
Author(s):  
Jenny Manuela Tabbert ◽  
Hartwig Schulz ◽  
Andrea Krähmer
Keyword(s):  
Broad Spectrum ◽  
Energy Savings ◽  
Light Emitting Diode ◽  
Thymus Vulgaris ◽  
Light Spectrum ◽  
Light Emitting ◽  
Biomass Yields ◽  
Lighting Systems

A light-emitting diode (LED) system covering plant-receptive wavebands from ultraviolet to far-red radiation (360 to 760 nm, “white” light spectrum) was investigated for greenhouse productions of Thymus vulgaris L. Biomass yields and amounts of terpenoids were examined, and the lights’ productivity and electrical efficiency were determined. All results were compared to two conventionally used light fixture types (high-pressure sodium lamps (HPS) and fluorescent lights (FL)) under naturally low irradiation conditions during fall and winter in Berlin, Germany. Under LED, development of Thymus vulgaris L. was highly accelerated resulting in distinct fresh yield increases per square meter by 43% and 82.4% compared to HPS and FL, respectively. Dry yields per square meter also increased by 43.1% and 88.6% under LED compared to the HPS and FL lighting systems. While composition of terpenoids remained unaffected, their quantity per gram of leaf dry matter significantly increased under LED and HPS as compared to FL. Further, the power consumption calculations revealed energy savings of 31.3% and 20.1% for LED and FL, respectively, compared to HPS. In conclusion, the implementation of a broad-spectrum LED system has tremendous potential for increasing quantity and quality of Thymus vulgaris L. during naturally insufficient light conditions while significantly reducing energy consumption.

Energy Benchmarking of Water and Wastewater Treatment, Distribution and Collection: A Case Study of Austin Water Utility

2013 ◽  
Author(s):  
Jill B. Kjellsson ◽  
David Greene ◽  
Raj Bhattarai ◽  
Michael E. Webber
Keyword(s):  
Water Distribution ◽  
Energy Intensity ◽  
Potable Water ◽  
Energy Savings ◽  
Operating Cost ◽  
Electric Energy ◽  
Water And Wastewater Treatment ◽  
Water Utility ◽  
Water And Wastewater

Nationally, 4% of electricity usage goes towards moving and treating water and wastewater. The energy intensity of the water and wastewater utility sector is affected by many factors including water source, water quality, and the distance and elevation that water must be transported. Furthermore, energy accounts for 10% or more of a utility’s total operating cost, suggesting that energy savings can account for significant cost savings. Better knowledge of where and when energy is used could support strategic energy interventions and reveal opportunities for efficiency. Accordingly, this investigation quantifies energy intensity by process and type, including electricity and natural gas, and explores the time-varying nature of electric energy consumption for potable water distribution using the Austin Water Utility (AWU) in Austin, Texas as a case study. This research found that most of energy consumed by the AWU is for pumping throughout the distribution network (57%) and at lift stations (10%) while potable water treatment accounts for the least (5%). Though the focus is site specific, the methodology shown herein can be applied to other utilities with sufficient data.

scholarly journals CASE STUDY OF ENERGY CONSERVATION IN INDUSTRY BY APPLICATION OF LEAN TOOLS

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
N.Sujith Prasanna ◽  
Dr.J.Nagesh Kumar
Keyword(s):  
Energy Conservation ◽  
Energy Saving ◽  
Energy Cost ◽  
Energy Use ◽  
Energy Savings ◽  
Sustainable Manufacturing ◽  
Integrated Approach ◽  
Energy Saving Potential ◽  
Lean Tools

Energy cost is significant in many of the manufacturing activities. The efficiency of energy use is quiet low as there are substantial visible and hidden losses. Visible losses can be easily identified and corrective action can be taken. However hidden and indirect losses form a sizeable portion of the losses. Identifying these losses is not easy and requires an integrated approach which includes thorough study of process, operations and their interactions with energy use. Industries across sectors have implemented lean management principles which target various wastes occurring in the plant. This paper discusses case studies which highlight the exploitation of lean tools as a means for unearthing hidden energy saving potential that often go unnoticed. In addition to the energy savings which results in improved profits and competitiveness, the approach also aids the industry to pursue a path of sustainable manufacturing.

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

2020 ◽  
Author(s):  
Jan Drgona ◽  
Lieve Helsen ◽  
Draguna L. Vrabie
Keyword(s):  
Neural Network ◽  
Energy Savings ◽  
Control Policy ◽  
Building Stock ◽  
Simulation Based ◽  
The Neural Networks ◽  
Promising Solution ◽  
Simple Evaluation ◽  
Energy Efficient Building

Abstract It has been shown that model predictive control (MPC) is a promising solution for energy-efficient building operations. However, the deployment of MPC in a large portion of the building stock has not been possible partially because of high installation costs. Every building is unique and requires a tailored MPC solution. The best performing solutions are often based on physics-based modeling, which is, however, computationally expensive and requires dedicated software. A promising direction that tackles this problem is to train a neural network-based optimal control policy to imitate the behavior of physics-based MPC from the simulation data generated offline. The neural networks give control actions that closely approximate those produced by physics-based MPC, but with a fraction of the computational and memory requirements and without the need for licensed software. The main advantage of the proposed approach stems from simple evaluation at execution time, leading to low computational foot-prints and easy deployment on embedded HW platforms. In the case study, we present the energy savings potential of physics-based MPC applied to an office building in Belgium. We demonstrate how neural network approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the remaining challenges and open research questions.

Jaya Optimization Algorithm Implemented on A New Novel Design of 6-DOF AUV Body: A Case Study

2021 ◽  
Author(s):  
Noorulden Basil ◽  
Hamzah M. Marhoon ◽  
Ahmed R. Ibrahim
Keyword(s):  
Optimization Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Optimization Technique ◽  
Pid Controllers ◽  
The Novel ◽  
Proportional Integral Derivative ◽  
New Novel ◽  
Novel Design ◽  
Optimum Output

Abstract The Novel Jaya Optimization Algorithm (JOA) was utilized in this research to evaluate the efficiency of a new novel design of Autonomous Underwater Vehicle (AUV). The Three Proportional Integral Derivative (PID) controllers were used to obtain the optimum output for the AUV Trajectory, which can be considered as a main side of the research for solving the AUV Performance. The optimization technique has been developed to solving the motion model of the AUV in order to reduce the rotations of trajectory for the AUV 6-DOF Body in the axis’s in x, y and z for the overall positions, velocity... etc., and to execute the optimum output for the dynamic kinematics model based on the Novel Euler-6 DOF AUV Body Equation implemented on MATLAB R2021a Version.

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