Maximized energy expenditure -
By optimizing energy use in these processes, facilities can improve efficiency and reduce costs, leading to increased competitiveness in the market.
Many facilities are implementing strategies such as energy-efficient lighting, HVAC systems, and building controls to minimize energy waste and improve efficiency. Governments around the world have also introduced regulations to promote energy efficiency, further increasing its importance in facilities.
These regulations often require facilities to meet specific energy-use targets, making it essential for organizations to monitor and improve energy efficiency to comply with these regulations. Industrial processes heavily rely on a multitude of interconnected equipment and machinery, each playing a vital role in the overall production cycle.
However, when equipment failures occur, they not only disrupt the workflow but also have a profound impact on energy consumption within industrial facilities. Equipment failures often lead to unexpected downtime, during which machinery is either completely halted or operates sub-optimally. This downtime results in a considerable loss of energy efficiency, as the systems are idling or running inefficiently without contributing to productive output.
Faulty equipment may operate outside of optimal parameters, leading to increased energy consumption. For example, a malfunctioning motor may draw more power than necessary, resulting in higher energy costs and a negative environmental impact.
Continuous operation of malfunctioning equipment can exacerbate wear and tear, leading to a higher frequency of breakdowns. This vicious cycle not only contributes to increased maintenance costs but also escalates energy consumption due to inefficient operation. Predictive maintenance emerges as a strategic solution to mitigate the impact of equipment failures on energy efficiency.
By leveraging advanced monitoring technologies and data analytics, organizations can identify potential issues before they escalate into critical failures.
Several manufacturing industries have reported significant reductions in energy consumption after implementing predictive maintenance strategies. Case studies show that identifying and rectifying energy-related equipment failures promptly leads to more efficient operation and lower overall energy costs.
In facilities where Heating, Ventilation, and Air Conditioning HVAC systems play a crucial role, predictive maintenance has proven to be instrumental in optimizing energy usage.
Early detection of issues in HVAC components ensures that the systems operate at peak efficiency. Energy monitoring serves as a cornerstone for achieving optimal energy efficiency in industrial applications. Real-time data acquisition involves the continuous collection and analysis of energy-related metrics to provide a dynamic view of energy consumption within a facility.
Smart metering technologies play a pivotal role in real-time data acquisition. These devices capture energy consumption data at frequent intervals, allowing organizations to monitor usage patterns, identify peak demand periods, and respond promptly to deviations from expected energy profiles.
Utilizing advanced data logging systems enables the capture of granular data on energy consumption. This data includes information such as voltage levels, current flow, and power factor, providing a comprehensive understanding of how energy is utilized across various processes and equipment.
Energy monitoring systems often integrate with Supervisory Control and Data Acquisition SCADA systems, facilitating seamless communication between energy data and the industrial control infrastructure.
This integration enhances the visibility of energy-related parameters for improved decision-making. Sensor technologies play a crucial role in the accurate and detailed monitoring of energy consumption.
Various sensors are employed to capture data related to equipment performance, environmental conditions, and overall energy usage. These sensors monitor the quality of electrical power, detecting issues such as voltage fluctuations, harmonics, and power factor.
By addressing power quality issues promptly, organizations can prevent equipment damage and enhance overall energy efficiency. Monitoring environmental conditions , such as temperature and humidity, is essential for understanding their impact on energy consumption.
For instance, optimizing HVAC systems based on real-time environmental data contributes to energy savings. Energy monitoring systems often leverage the Industrial Internet of Things IoT by integrating with a network of connected devices.
This interconnected ecosystem allows for comprehensive data collection, enabling a holistic view of energy usage and performance.
Integrating energy monitoring with predictive maintenance enables the continuous monitoring of equipment performance. Real-time anomaly detection identifies deviations from normal operating conditions, allowing for immediate intervention to address potential issues before they impact energy efficiency.
Energy monitoring systems facilitate trend analysis by tracking energy consumption patterns over time. This historical data enables predictive maintenance algorithms to identify emerging trends and anticipate future issues, supporting proactive maintenance strategies.
The integration of energy monitoring data enhances the precision of predictive maintenance models. By incorporating real-time energy consumption metrics, predictive algorithms can generate more accurate predictions regarding equipment health and potential failures. Energy monitoring contributes to the optimization of maintenance schedules by providing insights into the actual condition of equipment.
This allows organizations to prioritize maintenance activities based on real-time energy-related data, reducing unnecessary downtime and associated costs.
Energy efficiency and energy monitoring go along with one another. The process of measuring, following, and assessing energy use in a facility is referred to as energy monitoring. On the other hand, energy efficiency refers to minimizing energy waste and optimizing energy utilization within a facility.
Energy monitoring offers a thorough insight of how much energy is utilized in a facility, which may be used to spot inefficient and wasteful energy use. Energy monitoring may give a thorough picture of energy use and assist facilities in finding possibilities to increase energy efficiency by evaluating energy use in real-time.
Energy monitoring is applicable to many different forms of rotating machinery , including motors, pumps, compressors, and other kinds of mechanical machinery. Facilities can identify spots where energy is being wasted, such as equipment that is misaligned, overloaded, or running outside of its ideal range, by monitoring the energy consumption of these components.
Additionally, by keeping an eye on the energy usage of rotating equipment, facilities can see opportunities to enhance the performance of these parts, such as slowing down pumps outside of peak usage times or realigning motors to use less energy.
By using energy saving techniques, facilities can increase the energy efficiency of all rotating equipment and minimize energy waste.
According to the techniques used, chronic problems can be prevented by determining which equipment or process originates from the energy waste. To illustrate the impact of energy monitoring on maintenance costs with an example, compressed air leaks are an important source of wasted energy in air systems in industry.
The operating cost of an air compressor is more than the initial purchase. Industrial air compressors consume a lot of energy. When they are not working as efficiently as they should, some of this energy use does not contribute to the operation of the facility. While the increase in energy consumption in the compressors can be monitored by energy monitoring, on the other hand, it allows root-cause analysis to be made on the side of whether the increase in energy consumption from the current and voltage signals used in energy monitoring is due to gas leakage in the line or a fault on the machine.
Sustainability may be greatly aided by energy monitoring in the industrial sector, especially for rotating machinery. Energy monitoring offers useful information on energy use, which may be used to execute energy-saving measures that both directly and indirectly lower carbon emissions related to energy production.
Energy monitoring can also encourage the industrial sector to embrace green technology. Facilities can evaluate the effectiveness of various energy-saving measures and decide which technologies to install to further increase energy efficiency by monitoring energy use over time.
This can involve implementing energy-saving technology, such as energy recovery systems. By offering data-driven insights into energy use, energy monitoring of rotating equipment in the industrial sector may assist facilities in achieving their sustainability goals. Facilities may effectively promote sustainability by reducing carbon emissions, financial and environmental costs of energy by putting energy-saving measures into place and utilizing green technology.
Sensemore performs energy monitoring applications by calculating the power consumed by motors driving rotating equipment. For power calculations, current and voltage information from the phase cables feeding the motor is taken through analog sensors.
Since the collected analog data includes the phase difference information between voltage and current, the power factor is also calculated. The time that it takes for the forager to travel from the nesting site to the foraging site is an example of a constraint.
The maximum number of food items a forager is able to carry back to its nesting site is another example of a constraint. There could also be cognitive constraints on animals, such as limits to learning and memory.
Given the hypotheses about the currency and the constraints, the optimal decision rule is the model's prediction of what the animal's best foraging strategy should be.
Figure 1, shows an example of how an optimal decision rule could be determined from a graphical model. Energy gain per cost is the currency being optimized.
The constraints of the system determine the shape of this curve. Optimal foraging models can look very different and become very complex, depending on the nature of the currency and the number of constraints considered.
However, the general principles of currency, constraints, and optimal decision rule remain the same for all models. To test a model, one can compare the predicted strategy to the animal's actual foraging behavior.
If the model fits the observed data well, then the hypotheses about the currency and constraints are supported. If the model does not fit the data well, then it is possible that either the currency or a particular constraint has been incorrectly identified.
Optimal foraging theory is widely applicable to feeding systems throughout the animal kingdom. Under the OFT, any organism of interest can be viewed as a predator that forages prey.
There are different classes of predators that organisms fall into and each class has distinct foraging and predation strategies. The optimization of these different foraging and predation strategies can be explained by the optimal foraging theory. In each case, there are costs, benefits, and limitations that ultimately determine the optimal decision rule that the predator should follow.
One classical version of the optimal foraging theory is the optimal diet model , which is also known as the prey choice model or the contingency model. In this model, the predator encounters different prey items and decides whether to eat what it has or search for a more profitable prey item.
The model predicts that foragers should ignore low profitability prey items when more profitable items are present and abundant. The profitability of a prey item is dependent on several ecological variables. E is the amount of energy calories that a prey item provides the predator.
Handling time h is the amount of time it takes the predator to handle the food, beginning from the time the predator finds the prey item to the time the prey item is eaten.
Additionally, search time S is the amount of time it takes the predator to find a prey item and is dependent on the abundance of the food and the ease of locating it.
Using these variables, the optimal diet model can predict how predators choose between two prey types: big prey 1 with energy value E 1 and handling time h 1 , and small prey 2 with energy value E 2 and handling time h 2.
In order to maximize its overall rate of energy gain, a predator must consider the profitability of the two prey types. Thus, if the predator encounters prey 1 , it should always choose to eat it, because of its higher profitability. It should never bother to go searching for prey 2.
However, if the animal encounters prey 2 , it should reject it to look for a more profitable prey 1 , unless the time it would take to find prey 1 is too long and costly for it to be worth it. Since it is always favorable to choose to eat prey 1 , the choice to eat prey 1 is not dependent on the abundance of prey 2.
But since the length of S 1 i. how difficult it is to find prey1 is logically dependent on the density of prey 1 , the choice to eat prey 2 is dependent on the abundance of prey 1. The optimal diet model also predicts that different types of animals should adopt different diets based on variations in search time.
This idea is an extension of the model of prey choice that was discussed above. This rearranged form gives the threshold for how long S 1 must be for an animal to choose to eat both prey 1 and prey 2. In nature, generalists include a wide range of prey items in their diet.
These types of animals are defined as specialists and have very exclusive diets in nature. Additionally, since the choice to eat prey2 is dependent on the abundance of prey1 as discussed earlier , if prey1 becomes so scarce that S1 reaches the threshold, then the animal should switch from exclusively eating prey1 to eating both prey1 and prey2.
As previously mentioned, the amount of time it takes to search for a prey item depends on the density of the prey. Functional response curves show the rate of prey capture as a function of food density and can be used in conjunction with the optimal diet theory to predict foraging behavior of predators.
There are three different types of functional response curves. For a Type I functional response curve, the rate of prey capture increases linearly with food density. At low prey densities, the search time is long. Since the predator spends most of its time searching, it eats every prey item it finds.
As prey density increases, the predator is able to capture the prey faster and faster. At a certain point, the rate of prey capture is so high, that the predator doesn't have to eat every prey item it encounters. For a Type II functional response curve, the rate of prey capture negatively accelerates as it increases with food density.
In other words, as the food density increases, handling time increases. At the beginning of the curve, rate of prey capture increases nearly linearly with prey density and there is almost no handling time. As prey density increases, the predator spends less and less time searching for prey and more and more time handling the prey.
The rate of prey capture increases less and less, until it finally plateaus. The high number of prey basically "swamps" the predator. A Type III functional response curve is a sigmoid curve. The rate of prey capture increases at first with prey density at a positively accelerated rate, but then at high densities changes to the negatively accelerated form, similar to that of the Type II curve.
The predator is able to be choosy and does not eat every item it finds. However, at low prey densities the bottom of the curve the rate of prey capture increases faster than linearly.
This phenomenon is known as prey switching. Predator—prey coevolution often makes it unfavorable for a predator to consume certain prey items, since many anti-predator defenses increase handling time.
In addition, because toxins may be present in many prey types, predators include a lot of variability in their diets to prevent any one toxin from reaching dangerous levels. Thus, it is possible that an approach focusing only on energy intake may not fully explain an animal's foraging behavior in these situations.
The marginal value theorem is a type of optimality model that is often applied to optimal foraging. This theorem is used to describe a situation in which an organism searching for food in a patch must decide when it is economically favorable to leave.
While the animal is within a patch, it experiences the law of diminishing returns , where it becomes harder and harder to find prey as time goes on. This may be because the prey is being depleted, the prey begins to take evasive action and becomes harder to catch, or the predator starts crossing its own path more as it searches.
The curve starts off with a steep slope and gradually levels off as prey becomes harder to find. Another important cost to consider is the traveling time between different patches and the nesting site.
An animal loses foraging time while it travels and expends energy through its locomotion. In this model, the currency being optimized is usually net energy gain per unit time. The constraints are the travel time and the shape of the curve of diminishing returns.
Graphically, the currency net energy gain per unit time is given by the slope of a diagonal line that starts at the beginning of traveling time and intersects the curve of diminishing returns Figure 3.
In order to maximize the currency, one wants the line with the greatest slope that still touches the curve the tangent line. The place that this line touches the curve provides the optimal decision rule of the amount of time that the animal should spend in a patch before leaving.
Oystercatcher mussel feeding provides an example of how the optimal diet model can be utilized. Oystercatchers forage on mussels and crack them open with their bills.
The constraints on these birds are the characteristics of the different mussel sizes. While large mussels provide more energy than small mussels, large mussels are harder to crack open due to their thicker shells.
This means that while large mussels have a higher energy content E , they also have a longer handling time h. The oystercatchers must decide which mussel size will provide enough nutrition to outweigh the cost and energy required to open it.
They came up with a bell-shaped curve, indicating that moderately sized mussels were the most profitable. However, they observed that if an oystercatcher rejected too many small mussels, the time it took to search for the next suitable mussel greatly increased.
This observation shifted their bell-curve to the right Figure 4. However, while this model predicted that oystercatchers should prefer mussels of 50—55 mm, the observed data showed that oystercatchers actually prefer mussels of 30—45 mm.
Meire and Ervynk then realized the preference of mussel size did not depend only on the profitability of the prey, but also on the prey density. After this was accounted for, they found a good agreement between the model's prediction and the observed data.
The foraging behavior of the European starling , Sturnus vulgaris , provides an example of how marginal value theorem is used to model optimal foraging.
Starlings leave their nests and travel to food patches in search for larval leatherjackets to bring back to their young. The starlings must determine the optimal number of prey items to take back in one trip i. the optimal load size. While the starlings forage within a patch, they experience diminishing returns: the starling is able to hold only so many leatherjackets in its bill, so the speed with which the parent picks up larvae decreases with the number of larvae that it already has in its bill.
Thus, the constraints are the shape of the curve of diminishing returns and the travel time the time it takes to make a round trip from the nest to a patch and back. In addition, the currency is hypothesized to be net energy gain per unit time. Kacelnik et al.
wanted to determine if this species does indeed optimize net energy gain per unit time as hypothesized. The researchers artificially generated a fixed curve of diminishing returns for the birds by dropping mealworms at successively longer and longer intervals.
The birds continued to collect mealworms as they were presented, until they reached an "optimal" load and flew home. As Figure 5 shows, if the starlings were maximizing net energy gain per unit time, a short travel time would predict a small optimal load and a long travel time would predict a larger optimal load.
In agreement with these predictions, Kacelnik found that the longer the distance between the nest and the artificial feeder, the larger the load size. In addition, the observed load sizes quantitatively corresponded very closely to the model's predictions. Other models based on different currencies, such as energy gained per energy spent i.
energy efficiency , failed to predict the observed load sizes as accurately. Thus, Kacelnik concluded that starlings maximize net energy gain per unit time. This conclusion was not disproved in later experiments.
Worker bees provide another example of the use of marginal value theorem in modeling optimal foraging behavior. Bees forage from flower to flower collecting nectar to carry back to the hive. While this situation is similar to that of the starlings, both the constraints and currency are actually different for the bees.
A bee does not experience diminishing returns because of nectar depletion or any other characteristic of the flowers themselves.
Maximizing Energy Maximized energy expenditure and Diabetic retinopathy retinal damage Energy in British Columbia was co-authored by Pollution Probe and Body cleanse tea Pembina Institute. Expendityre provides an overview of energy Maximizwd in BC and recommends that increasing expendlture should be met first wxpenditure increasing our energy efficiency and then by Mxaimized renewable sources of energy. The report compares planned efficiency actions with forecast potential, provides information on the current status of renewable technologies in the province and provides some examples of how different groups and jurisdictions have been working to increase energy efficiency and renewable energy. The report offers a series of recommendations for the provincial government aimed at maximizing BC's energy efficiency and renewable energy potential. These recommendations were written in conjunction with the Canadian Renewable Energy Alliance CanREA based on the findings of the report and are available at www.Optimal Maaximized theory OFT is a behavioral ecology model that helps predict how an animal behaves when searching Collagen Rich Foods food.
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One classical Satiety and blood sugar control of the optimal foraging theory is the optimal diet modelwhich is also known as the prey choice model or the contingency model. In this model, the predator encounters different prey items and decides whether to eat what it has or search for a more profitable prey item.
The model predicts that foragers should ignore low profitability prey items when more profitable items are present and abundant. The profitability of a prey item is dependent on several ecological variables. E is the amount of energy calories that a prey item provides the predator.
Handling time h is the amount of time it takes the predator to handle the food, beginning from the time the predator finds the prey item to the time expendityre prey item is eaten.
Additionally, search time S is the amount of time it takes the predator to find a prey item and is dependent on the abundance of the food and the ease of locating it. Using these variables, the optimal diet model can predict how predators choose between two prey types: big prey 1 with energy value E 1 and handling time h 1and small prey 2 with energy value E 2 and handling time h 2.
In order to maximize its overall rate of energy gain, a predator must consider the profitability of the two prey types. Thus, if the predator encounters prey 1it should always choose to eat it, because of its higher profitability. It should never bother to go searching for prey 2. However, if the animal encounters prey 2it should reject it to look for a more profitable prey 1unless the time it would take to find prey 1 is too long and costly for it to be worth it.
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But since the length of S 1 i. how difficult it is to find prey1 is logically dependent on the density of prey 1the choice to eat prey 2 is dependent on the abundance of prey 1. The optimal diet model also predicts that different types of animals should adopt different diets based on variations in search time.
This idea is an extension of the model of prey choice that was discussed above. This rearranged form gives the threshold for how long S 1 must be for an animal to choose to expenditurf both prey 1 and prey dnergy. In nature, generalists include a wide range of prey items in their diet.
These types of animals are defined as specialists and have very exclusive diets in nature. Additionally, since the choice to eat prey2 is dependent on the abundance epxenditure prey1 as discussed earlierif prey1 becomes so scarce that S1 reaches the threshold, then the animal should switch from exclusively eating prey1 to eating both prey1 and prey2.
As previously mentioned, the amount of time it takes to search for a prey item depends on the density of the prey. Functional response curves show the rate of prey capture as a function of food density and can be used in conjunction with the optimal diet theory to predict foraging ennergy of predators.
There are three different types of functional response curves. For a Type I functional response curve, the rate of prey capture increases linearly with food density. At low prey densities, the search time is long.
Since the predator spends most of its time searching, it eats every prey item it finds. As prey density increases, the predator is able to capture the prey faster and faster. At a certain point, the rate of prey capture is so high, that the predator doesn't have to eat every prey item it encounters.
For a Type II functional response curve, the rate of prey capture negatively accelerates as it increases with food density. In other words, as the food density increases, handling Masimized increases. At the beginning of the curve, rate of prey capture increases nearly linearly with prey density and there is almost no handling time.
As prey density increases, the predator spends less and less time searching for prey and more and more time handling the prey. The rate of prey capture increases less and less, until it finally plateaus. The high number of prey basically "swamps" the predator.
A Type III functional response curve is a sigmoid curve. The rate of prey capture increases at first with prey density at a positively accelerated rate, but then at high densities changes to the negatively accelerated form, energgy to that of the Type II curve.
The predator is able to be choosy and does not eat every item it finds. However, at Maximozed prey densities the bottom of the curve the rate of prey capture increases faster than linearly. This phenomenon is known as prey switching. Predator—prey coevolution often makes it unfavorable for a predator to consume certain prey items, since many anti-predator defenses increase handling time.
In addition, because toxins may be present in many prey types, predators include a lot of variability in their diets to prevent any one toxin from reaching dangerous levels. Thus, it is possible that an approach focusing only on energy intake may not fully explain an animal's foraging behavior in these situations.
The marginal value theorem is a type of optimality model that is often applied to optimal foraging. This theorem is used to describe a situation in which expenditur organism searching for food in a patch must decide when it is economically favorable to leave.
While the animal is within a patch, it experiences the law of diminishing returnswhere it becomes harder and harder to find prey as time goes on.
This may be because the prey is being depleted, the prey begins to take evasive action and becomes harder to catch, or the predator starts crossing its own path more as it searches. The curve starts off with a steep slope and gradually levels off as prey becomes harder to find.
Another important cost to consider is the traveling time between different patches and the nesting site. An animal loses foraging time while it travels and expends energy through its locomotion.
In this Mzximized, the currency being optimized is usually net energy gain per unit time. The constraints are the travel time and the shape of the Maixmized of diminishing returns. Graphically, the currency net energy gain per unit time is given by the slope of a diagonal line that starts at the beginning of traveling time and intersects the Maximozed of diminishing returns Figure 3.
In order to maximize the currency, one wants the line with the greatest slope that still touches the curve the tangent line. The place that this line touches the curve provides the optimal decision rule of the amount of time that the animal should spend in a patch before leaving.
Oystercatcher mussel feeding provides an example of how the optimal diet model can be utilized. Oystercatchers forage on mussels and crack them open with their bills. The constraints on these birds are the characteristics of the different mussel sizes.
While large mussels provide more energy than small mussels, large mussels are harder to crack open due to their thicker shells. This means that while large mussels have a higher energy content Ethey also have a longer handling time h. The oystercatchers must decide which mussel size will provide enough nutrition to outweigh the cost and energy required to open it.
They came up with a bell-shaped curve, indicating that moderately sized mussels were the most profitable. However, they observed that if an oystercatcher rejected too many small mussels, the time it took to search for the next suitable mussel greatly increased.
: Maximized energy expenditure| Maximizing Warehouse Energy Efficiency | HTX Material Handling | commercial building energy use nearly 3 quadrillion Btu annually. Text version. There are three key reasons small business owners should improve the energy efficiency of a building:. Reduce operating costs and utility bills while increasing potential rental income and the overall property value. Enhance the marketability of your products, services, and brand image, particularly with environmentally conscientious customers. Reduce CO 2 emissions while improving customer experience and comfort. NREL helps small building and small business owners by demystifying the three-step processes and providing tools and resources needed to overcome obstacles to achieving building energy efficiency. Identify low- or no-cost energy savings opportunities. Benchmark building energy performance. Talk about the timing of building or equipment upgrades. Positive and negative cash flows. Understanding the risks and how to manage them. Finding financial incentives for energy efficiency and renewable energy strategies. Researching financing options and other programs, including those offered by the U. Small Business Administration SBA. Once your business case is in place, get more information about available programs. Resources include:. Resources and Case Studies on Energy Efficiency The U. Department of Energy's DOE's Better Buildings Initiative hosts a catalogue of resources to support the adoption of energy-saving building technologies that include simple steps to saving energy in commercial buildings. Small Business-Specific Resources NREL's four-page guide helps small businesses understand the energy and non-energy benefits of energy-efficiency investments. These resources also help small businesses know what contractor and auditor qualifications to look for, identify low- and no-cost energy savings opportunities, and understand the decision process for energy-efficiency upgrades. Explore additional financing options through DOE's Better Buildings Financing Navigator. Here are quick links to top resources to help business owners achieve their energy-efficiency goals and save money:. NREL's four-page lender's guide with discussion on timing and low-cost methods for managing risk associated with energy-efficiency upgrades. NREL's borrower's guide and accompanying presentation. The Pembina Institute acknowledges that the work we steward and those we serve spans across many Nations. In the spirit of truth, justice, reconciliation, and to contribute to a more equitable and inclusive future for all of society, please see our path towards Reconciliation and prioritizing Indigenous leadership. Subscribe Subscribe Events Events Media Media. Home Focus Areas Publications Blog About Donate. Publication - Oct. Asset 20 Asset 4 Asset 8. Tags: Community Policy , British Columbia , Renewable Energy , Energy Efficiency. Related Publications Reexamining Rates for Remote Renewable Energy How integrating energy justice in power purchase agreements can accelerate an Indigenous-led clean energy transition Publication June 7, By Arthur Bledsoe , Katarina Savic. In this paper, we consider the intersection of justice and energy with a special focus on the role of governments, regulators, and utilities in creating a fairer system of for power purchase agreements for Indigenous clean energy proponents. As part of the discussion, we interrogate the Bonbright Principles and their role in maintaining systemic barriers which directly affect the uptake of clean energy progress in remote Indigenous communities. Kamloops needs to retrofit homes to reduce carbon pollution Infographic shows scale of retrofits required to become a zero-carbon community by Publication Feb. Let's connect Asset 4 Asset 8 Asset 16 Asset 21 Subscribe. Subscribe Our perspectives to your inbox. First name Last name Your email Comments. |
| Energy Expenditure During Basic Mobility and Approaches to Energy Conservation | Given the hypotheses about the currency and the constraints, the optimal decision rule is the model's prediction of what the animal's best foraging strategy should be. As previously mentioned, the amount of time it takes to search for a prey item depends on the density of the prey. Once your business case is in place, get more information about available programs. Request a Quote. Here are some of the many advantages of having sustainable warehouse energy efficiency: Helps the environment : Aside from helping yourself, improving your warehouse energy efficiency can significantly benefit the environment. |
| Optimising sprint interval exercise to maximise energy expenditure and enjoyment in overweight boys | For example, using a walker or a rollator can decrease the cost of transport and increase the efficiency of gait. Total energy expenditure TEE can be calculated as the sum of resting energy expenditure REE , activity energy expenditure AEE , and thermic effect of food TEF. In general, total energy expenditure can be estimated using either indirect or direct calorimetry, which measure oxygen consumption or heat production, respectively. However, this technique is used less commonly due to the need for sophisticated lab-based equipment. Instead, the most common technique involves a form of indirect calorimetry, using a facemask and metabolic cart to measure respiratory oxygen consumption and carbon dioxide production, as there is a direct, linear relationship between oxygen consumption and energy produced i. There are other techniques to approximate total energy expenditure when more sophisticated equipment is not available. Energy expenditure for physiologic and pathologic gait is presented in the following sections. Table 2 contrasts activities and subtypes with higher and lower energy expenditure. Increased energy expenditure in most neuromusculoskeletal pathologies is multifactorial. Locomotor strategies that promote stability, whether self-learned or taught, may impose additional cost. For instance, after a stroke , impaired motor control, muscle spasticity, abnormal motor strategies, altered kinetics and kinematics, 8 and impaired balance 15 increase the cost of locomotion. In cerebral palsy CP , spasticity and associated abnormal e. After a spinal cord injury SCI , reduced lower extremity strength and trunk control impose significant metabolic demands on the upper extremities to facilitate assistive aid enabled ambulation. The weight of orthotics can add to the energy demands. In amputees , both non-prosthetic and prosthetic ambulation impose significant metabolic demands. Generally, dysvascular, higher level, and bilateral lower limb amputation gait is more energy intensive. In addition, prosthesis design and alignment influence energy cost. In traumatic amputees, knee design, but not foot at slow to moderate walking speeds significantly changes energy consumption, with a hydraulic knee being superior to a single axis knee. The metabolic cost of joint misalignment is much greater in transfemoral amputees single-axis knee aligned 2 cm posterior compared with transtibial amputees ankle joint dorsiflexion and plantarflexion. Energy conservation becomes important especially in pathologies where the cardiopulmonary reserve is limited such as congestive heart failure or severe chronic obstructive pulmonary disease, for neuromuscular disorders such as multiple sclerosis or amyotrophic lateral sclerosis, and for generalized cachexia and fatigue associated with malignancy. Rehabilitation professionals have adopted a multimodal approach to energy conservation across levels of care inpatient, ambulatory, and community to minimize the daily occurrence of symptoms such as shortness of breath, pain, and fatigue, presented below. Table 3 summarizes interventions that impact energy expenditure with ambulation. The WHO International Classification of Functioning Model can be used to understand energy conservation training at the impairment, activity, and participation levels. At the impairment level, pharmacologic management of fatigue using amantadine, pemoline, and modafinil in multiple sclerosis, and carnitine and donepezil in malignancy is represented only by low-level evidence. At the activity level, strategies include organization to maximize access and minimize distance traveled, time management to separate out events temporally, task simplification, avoiding extremes of temperature, good body posture, and sitting. Aerobic conditioning programs have demonstrated reduced oxygen consumption during unassisted and assisted walking in multiple pathologies as well as in healthy aging. Strengthening and focused gait training help improve inefficient compensatory gait strategies as well as reduce energy expenditure. At the participation level, ergonomic modifications as well as social support to help with tasks are a key part of maximizing work and social ability. Yoga, Tai Chi, breathing exercises, and meditation have also been proposed as self-management methods. As an illustration of this concept, COM vertical displacement is reduced by shorter steps and flat gait, but overall metabolic expenditure is still increased. In addition, more steps per unit distance and increased cadence to maintain speed are both very inefficient. Hence, energy-conservative gait balances and minimizes a COM vertical displacement, b stance phase energetic cost optimizing timing of gait events , and c swing phase energy costs optimizing step length. Stabilization in environmental design can help to improve energy expenditure and ergonomics. Mobility aids such as canes, crutches, and walkers facilitate locomotion and reduce falls. Biomechanical benefits include a larger base of support, reduced lower limb loads, augmented gait initiation and stopping, and increased somatosensory input. However, they also have the potential to impose significant attentional and upper extremity load demands and can destabilize gait and prevent recovery during falls. This especially is true in cases of weakness, impaired neuromotor control, and excessive postural excursions. Cane use has been found to increase energy expenditure in healthy, young 28 users, hypothesized to be due to increased cognitive demands of learning a new task. Crutches promote swing-through gait that is more energy intensive but also more efficient due to increased speed compared with normal, reciprocating gait 8. The design of the walker influences energy cost, with a rollator minimizing energy cost compared with a non-wheeled walker. Manual Wheelchair propulsion techniques as well as design parameters weight, component positions and proportions, etc. can influence energy expenditure. Clinical complications of manual wheelchair propulsion such as rotator cuff pathology and other overuse injuries also can result in decreased performance and increased energy expenditure. Shoe design and inserts can also impact energy expenditure. Conventional limb and spinal orthoses add extra weight to the appendicular and axial skeleton and hence can increase energy expenditure. However, limb orthoses such as the spring leaf AFO can improve gait kinematics and thus reduce the overall cost of walking. Powered orthoses to facilitate locomotion are a new field of study, with feasibility studies documenting positive outcomes. Studies have shown mixed effects of surgical intervention on energy expenditure in pathologic gait, but there may be benefit in selecting and performing surgery appropriately in certain pathologies. For instance, single event multilevel surgeries in patients with CP have the potential to reduce energetic cost of long-term postsurgical locomotion. Our understanding of the field of mobility energetics and its implications for rehabilitation is evolving. Recent studies have aimed to leverage the energy conservation theory to develop new treatment protocols for patients. commercial building energy use nearly 3 quadrillion Btu annually. Text version. There are three key reasons small business owners should improve the energy efficiency of a building:. Reduce operating costs and utility bills while increasing potential rental income and the overall property value. Enhance the marketability of your products, services, and brand image, particularly with environmentally conscientious customers. Reduce CO 2 emissions while improving customer experience and comfort. NREL helps small building and small business owners by demystifying the three-step processes and providing tools and resources needed to overcome obstacles to achieving building energy efficiency. Identify low- or no-cost energy savings opportunities. Benchmark building energy performance. Talk about the timing of building or equipment upgrades. Positive and negative cash flows. Understanding the risks and how to manage them. Finding financial incentives for energy efficiency and renewable energy strategies. Researching financing options and other programs, including those offered by the U. Small Business Administration SBA. Once your business case is in place, get more information about available programs. Resources include:. Resources and Case Studies on Energy Efficiency The U. Department of Energy's DOE's Better Buildings Initiative hosts a catalogue of resources to support the adoption of energy-saving building technologies that include simple steps to saving energy in commercial buildings. Small Business-Specific Resources NREL's four-page guide helps small businesses understand the energy and non-energy benefits of energy-efficiency investments. These resources also help small businesses know what contractor and auditor qualifications to look for, identify low- and no-cost energy savings opportunities, and understand the decision process for energy-efficiency upgrades. Explore additional financing options through DOE's Better Buildings Financing Navigator. Here are quick links to top resources to help business owners achieve their energy-efficiency goals and save money:. NREL's four-page lender's guide with discussion on timing and low-cost methods for managing risk associated with energy-efficiency upgrades. NREL's borrower's guide and accompanying presentation. |
| The Benefits | Biomechanical benefits include a larger base of support, reduced lower limb loads, augmented gait initiation and stopping, and increased somatosensory input. However, they also have the potential to impose significant attentional and upper extremity load demands and can destabilize gait and prevent recovery during falls. This especially is true in cases of weakness, impaired neuromotor control, and excessive postural excursions. Cane use has been found to increase energy expenditure in healthy, young 28 users, hypothesized to be due to increased cognitive demands of learning a new task. Crutches promote swing-through gait that is more energy intensive but also more efficient due to increased speed compared with normal, reciprocating gait 8. The design of the walker influences energy cost, with a rollator minimizing energy cost compared with a non-wheeled walker. Manual Wheelchair propulsion techniques as well as design parameters weight, component positions and proportions, etc. can influence energy expenditure. Clinical complications of manual wheelchair propulsion such as rotator cuff pathology and other overuse injuries also can result in decreased performance and increased energy expenditure. Shoe design and inserts can also impact energy expenditure. Conventional limb and spinal orthoses add extra weight to the appendicular and axial skeleton and hence can increase energy expenditure. However, limb orthoses such as the spring leaf AFO can improve gait kinematics and thus reduce the overall cost of walking. Powered orthoses to facilitate locomotion are a new field of study, with feasibility studies documenting positive outcomes. Studies have shown mixed effects of surgical intervention on energy expenditure in pathologic gait, but there may be benefit in selecting and performing surgery appropriately in certain pathologies. For instance, single event multilevel surgeries in patients with CP have the potential to reduce energetic cost of long-term postsurgical locomotion. Our understanding of the field of mobility energetics and its implications for rehabilitation is evolving. Recent studies have aimed to leverage the energy conservation theory to develop new treatment protocols for patients. For instance, one recent study demonstrated that stroke survivors with hemiparetic, asymmetric walking patterns could learn to walk more symmetrically when the new gait pattern reduced the cost of transport. While energy conservation remains the most prevalent theory, it also has been suggested that human gait may be optimized to balance several other factors, such as muscle activity, stability, ground-reaction forces, and joint ligament use, 34,35 sometimes at the expense of energy efficiency. These other factors may explain in part why certain patient populations tend to default to movement patterns with increased energetic cost. Rehabilitation strategies for cost-efficient gait also are evolving, especially with emerging technologies. In addition to the traditional assistive devices and methods described above, there is a growing body of research involving newer technologies such as complete or partial exoskeletons. Such devices have the potential to improve mobility and independence in non-ambulatory people while minimizing energy cost. Finally, researchers are developing novel strategies increasingly to understand how the nervous system optimizes energetic cost of movement, including mechatronic 39 and biofeedback 33 -based systems that assess how people optimize their gait patterns in novel environments. From a functional perspective, the definition of mobility can range from crawling to walking to an instrumented activity such as driving. Thus, locomotion is only a subset of mobility, energetics is one subset of locomotion, and energy expenditure a subset of energetics. For the purposes of this article, concepts from kinetics and muscle energetics have been included only where they improve the explanation of energy expenditure in mobility and conservation. Owing to the vastness and evolving nature of the field, this article has focused primarily on normal and assisted locomotion, including brief sections on impairment, activities, and participation that supplement the holistic understanding of concepts relevant to energy expenditure and conservation. Prateek Grover, MD, PhD, Oksana Volshteyn MD. Energy expenditure during basic mobility and approaches to energy conservation. Prateek Grover, MD, PhD, MHA, Oksana Volshteyn MD. Richard D Zorowitz, MD Brainq, Research Grant, Principal Investigator Ipsen, Research Grant, Principal Investigator Ipsen, Honorarium, Advisory Board Spr, Therapeutics, Honorarium Data, Safety Monitor. Skip to content Search for:. Essentials of Rehabilitation Practice and Science. Racial Disparities in Access to and Outcomes from Rehabilitation Services Conceptual Models of Disability Environmental Assessment Functional Assessment The Early History of Physical Medicine and Rehabilitation in the United States The Philosophical Foundations of Physical Medicine and Rehabilitation Caregiver Education. Neurological recovery and neuromuscular physiology. Energy Expenditure During Basic Mobility and Approaches to Energy Conservation. Author s : Richard D Zorowitz, MD, Matthew A Statton, MD. Originally published: August 22, Last updated: December 28, Overview and Description Introduction Physiatrists are well versed in visual assessment of gait deviations that accompany musculoskeletal pathology, such as arthritis, and neurologic pathology, such as stroke. Energy conservation is one goal of human locomotion The human body can be modeled biomechanically as body segments linked at joints. Basics of energy expenditure relevant to mobility Energy metabolism involves the production of energy from the combustion of fuel such as carbohydrates, protein, or fat. Table 1. Energetics — Basic Definitions 5,7 Energy-Expenditure-Table Get published and recognized among your peers. Apply to be an Author. Pain and Placebo Physiology Assessment and Treatment of Balance Impairments Biomechanic of Gait and Treatment of Abnormal Gait Patterns. This website uses cookies to improve your experience. By using our website, you agree to our use of cookies. If you're curious about our policy, Cookie settings. Close Privacy Overview This website uses cookies to improve your experience while you navigate through the website. This observation shifted their bell-curve to the right Figure 4. However, while this model predicted that oystercatchers should prefer mussels of 50—55 mm, the observed data showed that oystercatchers actually prefer mussels of 30—45 mm. Meire and Ervynk then realized the preference of mussel size did not depend only on the profitability of the prey, but also on the prey density. After this was accounted for, they found a good agreement between the model's prediction and the observed data. The foraging behavior of the European starling , Sturnus vulgaris , provides an example of how marginal value theorem is used to model optimal foraging. Starlings leave their nests and travel to food patches in search for larval leatherjackets to bring back to their young. The starlings must determine the optimal number of prey items to take back in one trip i. the optimal load size. While the starlings forage within a patch, they experience diminishing returns: the starling is able to hold only so many leatherjackets in its bill, so the speed with which the parent picks up larvae decreases with the number of larvae that it already has in its bill. Thus, the constraints are the shape of the curve of diminishing returns and the travel time the time it takes to make a round trip from the nest to a patch and back. In addition, the currency is hypothesized to be net energy gain per unit time. Kacelnik et al. wanted to determine if this species does indeed optimize net energy gain per unit time as hypothesized. The researchers artificially generated a fixed curve of diminishing returns for the birds by dropping mealworms at successively longer and longer intervals. The birds continued to collect mealworms as they were presented, until they reached an "optimal" load and flew home. As Figure 5 shows, if the starlings were maximizing net energy gain per unit time, a short travel time would predict a small optimal load and a long travel time would predict a larger optimal load. In agreement with these predictions, Kacelnik found that the longer the distance between the nest and the artificial feeder, the larger the load size. In addition, the observed load sizes quantitatively corresponded very closely to the model's predictions. Other models based on different currencies, such as energy gained per energy spent i. energy efficiency , failed to predict the observed load sizes as accurately. Thus, Kacelnik concluded that starlings maximize net energy gain per unit time. This conclusion was not disproved in later experiments. Worker bees provide another example of the use of marginal value theorem in modeling optimal foraging behavior. Bees forage from flower to flower collecting nectar to carry back to the hive. While this situation is similar to that of the starlings, both the constraints and currency are actually different for the bees. A bee does not experience diminishing returns because of nectar depletion or any other characteristic of the flowers themselves. The total amount of nectar foraged increases linearly with time spent in a patch. However, the weight of the nectar adds a significant cost to the bee's flight between flowers and its trip back to the hive. Wolf and Schmid-Hempel showed, by experimentally placing varying weights on the backs of bees, that the cost of heavy nectar is so great that it shortens the bees' lifespan. Thus, there is a curve of diminishing returns for the net yield of energy that the hive receives as the bee gathers more nectar during one trip. The cost of heavy nectar also impacts the currency used by the bees. Unlike the starlings in the previous example, bees maximize energy efficiency energy gained per energy spent rather than net rate of energy gain net energy gained per time. This is because the optimal load predicted by maximizing net rate of energy gain is too heavy for the bees and shortens their lifespan, decreasing their overall productivity for the hive, as explained earlier. By maximizing energy efficiency, the bees are able to avoid expending too much energy per trip and are able to live long enough to maximize their lifetime productivity for their hive. The nature of prey selection by two centrarchids white crappie and bluegill has been presented as a model incorporating optimal foraging strategies by Manatunge and Asaeda. The predicted reactive distances were compared with experimental data. The energetic cost associated with fish foraging behaviour was calculated based on the sequence of events that takes place for each prey consumed. In most cases, the fish exclusively selected large Daphnia , ignoring evasive prey types Cyclops , diaptomids and small cladocera. This selectivity is the result of fish actively avoiding prey with high evasion capabilities even though they appear to be high in energetic content and having translated this into optimal selectivity through capture success rates. The energy consideration and visual system, apart from the forager's ability to capture prey, are the major determinants of prey selectivity for large-sized bluegill and white crappie still at planktivorous stages. Although many studies, such as the ones cited in the examples above, provide quantitative support for optimal foraging theory and demonstrate its usefulness, the model has received criticism regarding its validity and limitations. First, optimal foraging theory relies on the assumption that natural selection will optimize foraging strategies of organisms. However, natural selection is not an all-powerful force that produces perfect designs, but rather a passive process of selection for genetically based traits that increase organisms' reproductive success. Given that genetics involves interactions between loci , recombination , and other complexities, there is no guarantee that natural selection can optimize a specific behavioral parameter. In addition, OFT also assumes that foraging behaviors are able to be freely shaped by natural selection, because these behaviors are independent from other activities of the organism. For example, the need to avoid predators may constrain foragers to feed less than the optimal rate. Thus, an organism's foraging behaviors may not be optimized as OFT would predict, because they are not independent from other behaviors. Another limitation of OFT is that it lacks precision in practice. In theory, an optimal foraging model gives researchers specific, quantitative predictions about a predator's optimal decision rule based on the hypotheses about the currency and constraints of the system. However, in reality, it is difficult to define basic concepts like prey type, encounter rates, or even a patch as the forager perceives them. Furthermore, although the premise of OFT is to maximize an organism's fitness, many studies show only correlations between observed and predicted foraging behavior and stop short of testing whether the animal's behavior actually increases its reproductive fitness. It is possible that in certain cases, there is no correlation between foraging returns and reproductive success at all. One of the most imperative critiques of OFT is that it may not be truly testable. This issue arises whenever there is a discrepancy between the model's predictions and the actual observations. It is difficult to tell whether the model is fundamentally wrong or whether a specific variable has been inaccurately identified or left out. Because it is possible to add endless plausible modifications to the model, the model of optimality may never be rejected. Optimal foraging theory has been used to predict animal behaviour when searching for food, but can also be used for humans specifically hunter-gatherers. Food provides energy but costs energy to obtain. Foraging strategy must provide the most benefit for the lowest cost — it is a balance between nutritional value and energy required. The currency of optimal foraging theory is energy because it is an essential component for organisms, but it is also the downfall of optimal foraging theory in regard to archaeology. However, this can be hard to accepted in relation to complex animals with a high behavioral flexibility. Human behaviour is not always predictable when using the premise of optimal foraging theory — hunter-gatherers could for ritual or feasting purposes choose a game which would not benefit energy, but would benefit other needs. These changes can occur over a long time, over a season or during a hunt. Optimal foraging theory must therefore be more complex and introduces more goals and constraints to match the complex decision-making processes employed by humans. But optimal foraging theory has helped archaeology in generating new interpretations of patterns in the archaeological record and thinking about human behaviours in greater detail. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. Behavioral ecology model. doi : JSTOR University of California, Santa Cruz. and Krebs, J. Monographs in Behavior and Ecology. Princeton University Press. ISBN and Davies, N. Oxford: Blackwell Scientific Publications. Evolutionary Ecology. S2CID Bibcode : Natur. Princeton University Press, Princeton. Foraging: Behavior and Ecology. Chicago: University of Chicago Press. Ronald American Naturalist. PMID Integrative and Comparative Biology. First Edition ed. Cambridge UP, Ecological Monographs. Journal of Experimental Marine Biology and Ecology. Animal Behaviour. Patch Residence Time". The Journal of Animal Ecology. The American Naturalist. How birds choose among foraging modes". Bibcode : PNAS PMC Behavioral Ecology and Sociobiology. Alan C. Kamil, John R. Krebs, and H. |
| Maximizing Energy Efficiency and Renewable Energy in British Columbia | Pembina Institute | Finally, researchers are developing novel strategies increasingly to understand how the nervous system optimizes energetic cost of movement, including mechatronic 39 and biofeedback 33 -based systems that assess how people optimize their gait patterns in novel environments. These massive openings allow heat and air-conditioning to escape when in use. Wolf and Schmid-Hempel showed, by experimentally placing varying weights on the backs of bees, that the cost of heavy nectar is so great that it shortens the bees' lifespan. The nature of prey selection by two centrarchids white crappie and bluegill has been presented as a model incorporating optimal foraging strategies by Manatunge and Asaeda. The oystercatchers must decide which mussel size will provide enough nutrition to outweigh the cost and energy required to open it. |
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