STORING OXYGEN OR STORING ENERGY?

A COST-EFFECTIVENESS MODEL FOR COMPARING APPROPRIATE MEDICAL OXYGEN SUPPLY SYSTEMS IN LOW-

RESOURCE HEALTH FACILITIES WITH INTERMITTENT POWER

B.D. Bradley*, S. Qu*, D. Peel †, S.R.C. Howie§, Y.-L. Cheng*

*Centre for Global Engineering, Chemical Engineering & Applied Chemistry, University of Toronto, Canada † Ashdown Consultants, Hartfield, East Sussex, UK

§Medical Research Council Unit, The Gambia Correspondence: bev.bradley@mail.utoronto.ca

Keywords: oxygen, cost-effectiveness, childhood pneumonia, energy storage, oxygen storage, battery, solar.

Abstract A model is presented for comparing the cost-effectiveness of different oxygen technology systems specifically designed for health centres with intermittent power. We compare the options of storing energy in batteries vs. storing oxygen generated when energy is available. Conceptual oxygen storage system designs are presented. The model takes into account hours of grid power per day, oxygen demand (both annual and peak simultaneous demand), equipment costs and life span, and costs of electricity and maintenance. Cost per 1000L of oxygen delivered, annual costs, and initial capital costs are calculated and compared across technology options. The model can be applied generally, but results presented here use input parameters specific to The Gambia.

1 Introduction and Background Medical oxygen is essential in treating many diseases in the developing world including severe sepsis, malaria, and pneumonia - the leading cause of death in children under five worldwide [1]. Yet, in many developing countries, where the two primary oxygen supply options are compressed oxygen cylinders and oxygen concentrators, oxygen is still not widely available. Cylinders require costly refills and are difficult to transport; concentrators can be less costly to operate, but require a relatively high capital investment and a reliable power supply – a barrier for their widespread use, particularly in Sub-Saharan Africa [2],[3]. In The Gambia, the annual cost to supply oxygen to all hospitals and major health centres is over $150,000 USD via cylinders, and less than $20,000 USD via concentrators should electricity be available 24/7 [3]. Unfortunately over half the hospitals and major health centres have electricity for less than 12 hours per day from either grid power or generators [4]. Average power outage duration in 2009 ranged from 36 minutes to almost 4 hours for three Gambian health centres; with some outages lasting as long as 13.5 hours [5]. The Gambia is not unique; unreliable grid power remains a challenge in many low-income countries.

There is a need to find cost-effective, appropriate, and sustainable oxygen systems for health centres with limited financial resources and unreliable power [2], [3]. An integral step in this process is the development of tools to compare different technology configurations prior to field testing.

1.1. Cost-analysis models of medical oxygen technologies

One Nigerian study concluded that the annual cost for a fixed number of patients treated in neonatal ward was lower for concentrators than cylinders [6]. Their analysis accounted for: the recurring costs of cylinders, the discounted amortized annual cost of concentrators based on the initial capital cost and expected life span; and the scalability of costs for cylinders (cost number of patients) versus concentrators (cost number of required machines). Cylinder transport costs and power costs for concentrators were not considered.

A computer tool called OxOp was developed and applied in The Gambian context to determine the most suitable and lowest cost oxygen technology for individual health centres out of three options: concentrators with grid power, concentrators with a generator power, or cylinders [3]. The OxOp tool accounts for amortized capital costs, recurring cylinder costs, power costs, cylinder transport costs, and annual oxygen demand. Cost per 1000L of oxygen generated and annual cost are computed as measures of cost-effectiveness. This tool represents the first attempt at a systematic decision-support tool, based on cost-effectiveness, for oxygen supply options in a low-resource setting.

1.2. Oxygen systems for poor power: Field experiences

There is limited experience in using alternative energy sources to operate concentrators. Many hospitals and health centres have backup generators, but they are often only used in emergency situations. A battery system has been developed and is currently being field tested at a Gambian health centre [5]. The batteries supply power continuously to a concentrator with just four hours per day of grid charging time.

The only known example that uses solar panels to charge batteries to power a concentrator was reported from The Gambia in 2001 [7]. This isolated case showed that where

there is sufficient demand for oxygen (i.e. more than six treatment days per month at 1 litre per minute (LPM) or 1440L/day), a solar system can be a cost-effective alternative to cylinders based on a comparison of monthly operating costs. The initial capital investment required for solar technology may be expensive, but since the cost of solar technology has been decreasing rapidly (some sources estimate a drop of 60% in the last 3 years [8]), this option is included for comparative analysis in the current study.

We are aware of three commercial oxygen production plants for local generation, compression, and storage of oxygen. These self-contained systems offer an alternative to the energy storage approaches using batteries described above. Oxygen is generated and stored when power is available. The AirSep (Buffalo, NY) Ultrox system fills two H-size (7000L) cylinders per day, can operate at ambient temperatures up to 43°C, and costs $20,000 USD. The Invacare (Cleveland, OH) Homefill Ambulatory system and the Diamedica (Devon, UK) Oxygen Reservoir Filling System have lower oxygen production rates, which match the known demand and are also much less expensive. Experience with such systems in clinical settings in developing countries is very limited.

1.3. Objectives

Our main objective is to develop a model (i.e. framework and implemented computer tool) to compare the cost-effectiveness of storing energy vs. generating and storing oxygen for use when power is unavailable. To make this comparison, a secondary objective is to present different conceptual oxygen storage system designs. Building on the OxOp tool [3], our aim was to develop a comprehensive tool for comparing different oxygen system configurations over a wide range of possible input parameters, using a normalized set of performance measures. In this paper, we first present the oxygen storage system designs, with a description of two different delivery scenarios considered for analysis, followed by the two energy storage options. Then we present the framework of our cost comparison model, before presenting analysis methods and results specific to The Gambia.

2 Oxygen Storage Systems: Conceptual Designs Several functions are required for an oxygen storage system: local oxygen generation and compression into a pressurized storage vessel, the storage vessel itself, and an oxygen distribution mechanism. Two technological options have been considered for each of these functional stages, which can be combined in different permutations to produce various oxygen storage system designs (Figure 1). Only the first two functional stages are described in detail here.

2.1. Oxygen Generation and Compression:

Commercial oxygen production plant: We chose the AirSep Ultrox system as an example of a commercial system for oxygen storage in this study. Only high pressure cylinders can be used with the Ultrox. Other examples of this type of setup include the Invacare and Diamedica systems. The Diamedica uses low pressure reservoirs as storage vessels. An analysis of this option is planned for future work.

Self-Assembled Concentrator and Compressor: A “do-it-yourself” (DIY) oxygen storage system can be assembled using a conventional concentrator and a compressor to pressurize oxygen for storage. The capital cost of a self-assembled system is lower than an Ultrox; but technical feasibility, especially under field conditions, remains to be tested. It is included in this study for comparison purposes.

Figure 1: Major functional stages of an oxygen storage

system and their individual component design options.

2.2. Oxygen Storage Vessel

Oxygen Cylinders: Cylinders can be refilled with pressurized oxygen produced on-site, thus avoiding the need for transport from an oxygen plant. Cylinders are portable, available in different sizes, and are familiar to health center staff. Cylinders can be filled either from a commercial system or a self-assembled compressor system.

Oxygen Reservoir: An oxygen reservoir is a large, confined, compartment from which oxygen is delivered to patients through a piping system or directly through a regulated tubing system. Reservoirs will be at a lower pressure than cylinders, reducing energy consumption or even eliminating the need for a compressor, but the corresponding increased space requirement may be a challenge.

2.3. Conceptual designs included in model:

Our model allows for the analysis of all the different storage system combinations shown in Figure 1. Beyond these design choices, different delivery scenarios exist depending on whether, while grid power is available, patients receive oxygen (A) directly from the storage stock; or (B) from an extra concentrator operated by grid power. These are referred to as Scenarios A and B.

2.3.1. Scenario A (always draw from storage)

This setup is the direct analogue to a grid-charged battery system in that patients always draw oxygen from the ‘storage’ vessel regardless of power availability. When there is power, pressurized oxygen is produced and fed to the storage vessel, increasing the oxygen inventory. When power is interrupted oxygen production ceases and storage levels decline as patients draw on the store of oxygen. The inventory of stored oxygen ensures the continuity of oxygen supply to patients. A benefit of this scenario is oxygen can be dispensed to

exactly match demand, ensuring the utilization rate is 100% (see Sec. 4.1). A shortcoming of this design is that all oxygen generated undergoes energy-intensive compression, even when a concentrator could have delivered oxygen directly to patients, bypassing the compression step (as in Scenario B).

2.3.2. Scenario B (draw from storage when power is off)

This scenario uses concentrators to supply patients directly when there is power, and uses stored oxygen only when grid power is unavailable. It consists of the same three functional components, with the addition of extra concentrators for direct patient use. One anticipated challenge with this scenario is that patients would have to be switched between concentrator and stored oxygen as power goes in and out. This switch can be done at a switching manifold where stored oxygen is connected to the second input of a Sureflow flow-splitter. Oxygen flow may be briefly interrupted during the switch, especially if the power outage is unexpected.

3 Energy Storage Systems 3.1. Grid-charged battery system: the benchmark

The grid-charged battery system included in this analysis is based largely on the system developed in The Gambia, consisting of a charger, a battery bank, an inverter, and an oxygen concentrator [5]. The oxygen concentrator draws its power directly from a battery bank, via a DC-to-AC inverter. The batteries are recharged when mains power is available, and will discharge when there is no power if the concentrator is in use. In theory, each of the components in this system can be individually scaled to suit the specific application requirements. For example, battery capacity is chosen such that batteries will not be depleted below an ideal depth-of-discharge (DOD) of 50%, as this adversely impacts battery life. Our model selects the most appropriate components according to the input parameters provided (see Sec. 4.2).

3.2. Solar powered system

A solar-charged system consists of solar panels, a solar charge controller, a battery bank, and a DC-to-AC inverter [7]. When the solar array is activated by the sun, the charge controller will charge the batteries while sending current to the inverter to power the concentrator. When there is no solar irradiation, the battery bank will maintain concentrator operation until solar charging resumes – depleting the level of stored energy. As with the grid-charged system, our model will choose appropriately scaled components to suit the specific application requirements.

4 Model Description and Design Our oxygen system cost-analysis model was developed in Excel. An overview of the model framework (inputs, calculated parameters, and outputs) is shown in Figure 2.

4.1. Input Parameters

Hospital Parameters: The four hospital parameters are: number of annual child admissions, percent of admissions requiring oxygen, average flow rate prescription (LPM), and average treatment duration in days.

Figure 2: Oxygen System Cost-Analysis Model Framework

Technological Parameters: The technological parameters are: the power availability at the health facility of interest (grid and solar hours per day), the litres of oxygen which can be generated per hour of power, and three loss parameters (under-utilization rate, leakage, and electrical efficiency losses - for those systems using an inverter with batteries).

The utilization rate is the ratio between the estimated patient consumption rate (based on the hospital parameters) and the concentrator’s output flow-rate. Since concentrators are designed to produce a fixed level of output regardless of demand, output in excess of demand will be expelled. This wastage has not been quantified in the past. Our model includes an assessment of the impact of wastage on the cost-effectiveness of energy storage systems. There is no such wastage in oxygen storage options.

It is estimated that leakage can lead to the loss of anywhere from 10 to 80% of stored oxygen, depending on the quality, age, and standard of maintenance of the equipment, as well as whether oxygen is piped or delivered directly to patients [3]. For either cylinders or reservoirs, possible model inputs for leakage are: 10% for new, well-maintained equipment, 50% for old, poorly-maintained equipment, and 80% for old, poorly-maintained equipment with piping [3].

Financial parameters: Financial parameters include: amortized annual capital cost, electricity rate ($/kW), and maintenance expenses (cost per number of operational hours). Each of these parameters influences one of the three output cost components - capital, operation, and maintenance. All equipment specifications, and estimated capital and maintenance costs are based on correspondence with suppliers or reports from projects, and are documented.

4.2. Calculated Parameters

Oxygen demand: Hospital-specific inputs are used to calculate three parameters: total annual oxygen consumption (L/yr); and the cumulative daily (L/day) and hourly (L/hour) oxygen demand, which drive equipment sizing decisions.

Choice and quantity of equipment: As shown in Figure 2, system sizing (i.e. choice and quantity) decisions are based on the oxygen demand targets to be met and the technological inputs. For each system configuration, the model decides: 1) the optimal capacity for individual components from known available capacities (e.g. battery capacity, charger and inverter rating), and; 2) the quantity of components required to meet targets (e.g. number of Ultrox systems or concentrators). The model selects the most appropriate combination of components and capacities available. Exact details of the model logic for system sizing decisions are not provided here, but some key points are noted below.

Hourly demand (L/hr) drives decisions regarding oxygen delivery equipment; some components have a maximum capacity (e.g. 8 LPM concentrators and the Sureflow can supply up to 5 patients or 480L/hr). Cumulative daily demand influences the size of the backup store of energy or oxygen required to sustain the expected number of hours without power. We have enforced the design constraint that the backup capacity must sustain a number of hours of continual depletion equal to the time without power each day. This is achieved with a suitable battery bank capacity in energy storage systems; and an appropriate number of cylinders or reservoir volume in oxygen storage systems. The systems must also be capable of producing/storing these amounts of backup with the limited hours of power available per day.

4.3. Model Output

The key output variable, cost to deliver 1000L of oxygen, is comprised of three cost components: capital, operating, and maintenance cost. This output measure, computed for each oxygen system compared, represents how much it costs each system to reach the same oxygen demand targets. The 1000L considered is “consumable oxygen” for patients; the actual amount of oxygen produced could very well be higher to account for the loss factors we incorporated in the model (i.e. leakage, under-utilization). This measure was chosen to better relate the cost of operating these technologies with a clinical benefit (i.e. meeting the oxygen needs of the health facility).

The capital cost for 1000L of oxygen is determined by the sum of the amortized costs of each piece of equipment in a given system configuration divided by the annual volume of oxygen consumed. The operating cost per 1000L consumed is calculated by the electricity rate ($/KWh) divided by oxygen production efficiency (L/KWh), where production efficiency incorporates the loss factors. The maintenance cost per 1000L is calculated in either of two ways. For equipment that requires replacements and spare parts (i.e. compressors, the Ultrox, concentrators), the cost of maintenance is a function of total operating hours. For equipment which has a regular maintenance and check-up schedule regardless of how heavily it is used (i.e. cylinders), the cost of maintenance is a function of absolute time. Both are represented as a ratio between the maintenance cost over time ($/hr) and the production rate (L/hr) giving units of $/L.

4.4. Model Assumptions

Assumptions made in the model are: (1) only oxygen needs

for hypoxaemic children under five were considered; (2) operating efficiency, lifespan, and maintenance schedules are based on manufacturer specifications; no adjustment is made for the effect of low power quality, or high ambient temperature and humidity; (3) the same life-expectancy is applied to all pieces of the same type of equipment, regardless of how heavily they are used in different scenarios modeled; (4) all estimated equipment costs are exclusive of tax, shipping, and assembly, and no discount factor is used when calculating amortized capital costs; (5) solar panel cost is $1.40/W [8]; (6) electrical efficiency losses are 10%.

5 Analysis Methods To demonstrate the applicability of our model, we used The Gambia as a case example with the following input parameters: 6% of children admitted receive oxygen [9] and on average receive 0.5LPM for 3.6 days continuously [10]; 10 hours of grid power per day is typical with 4 hours being the worst-case scenario [4], [5]; 4.5 hours of solar charging hours are available per day [11]; electricity cost is $0.4/KWh [3]; and oxygen leakage is set to 10%.

For a mid-sized health centre in The Gambia (approximately 1290 under-5 admissions per year or 200,000L annually [4]), we compared four storage options: an Ultrox with cylinders, and a compressor with a reservoir – each for both Scenarios A and B. All systems use direct-to-patient delivery over piping. The two most cost-effective oxygen storage options were then compared to the energy storage systems. We also compared the sensitivity of cost per 1000L to two changing parameters: grid power availability (10 hours per day (default) vs. 4 hours per day), and; number of simultaneous patients needing oxygen (1, 3, or 6 – the “worst-case scenario” or peak simultaneous demand as estimated by statistical simulations1). Finally, we compared the sensitivity of annual cost to varying under-5 patient admission rates (500 to 6000 admissions per year), representing the range in The Gambia [4].

6 Results 6.1. Comparison of Storage Options in The Gambia

For the mid-sized Gambian health centre modeled, the Ultrox storage option with cylinders is more cost-effective than the self-assembled compressor configuration, for both Scenarios A and B, according to total annual cost and cost per 1000L (not shown). Amortized capital costs make up the majority of annual costs for all options, and the Ultrox has lower annual maintenance and operating costs. Scenario B offered no cost advantage over Scenario A for the Ultrox system, but did reduce the cost of the compressor system to be within a comparable range to the Ultrox.

6.2. Oxygen vs. Energy Storage in The Gambia

For a mid-sized health centre, both energy storage options have lower costs per 1000L compared to the Ultrox storage systems, with solar being the lowest (Figure 3). Initial capital

1Unpublished work estimating oxygen demand using simulation, taking into account seasonal trends in admission rates due to rainy season.

cost for solar is more than the grid-charged system, but both are still less than the Ultrox. Ultrox costs are high due to its over capacity at this low oxygen demand level.

Figure 3: Cost per 1000L ($USD) of different oxygen system

configurations (energy storage vs. oxygen storage).

Figure 4 shows system costs for different peak simultaneous demand situations: 1, 3 or 6 patients requiring oxygen at once. The lower and upper ends of the bar represent one patient and six patients, respectively. Unless otherwise labelled, the cost to service one or three patients is the same. Adjacent bars are for four and 10 hours of grid power per day. With 10 hours of power per day, the Ultrox storage systems are insensitive to fluctuations in simultaneous demand, meaning a health centre would be well prepared to meet most oxygen needs for the same cost. With only 4 hours of power, however, there are huge cost increases to serve higher peak demands; more oxygen must be generated and stored in a shorter amount of time, increasing equipment costs. The range in cost to meet low and high oxygen demand for the energy storage systems are similar, however the solar system is slightly lower overall, and independent of grid hours.

Figure 4: Sensitivity of cost per 1000L to grid power

availability (4 or 10 hours per day) and range in simultaneous patients needing oxygen (1, 3, or 6).

The cost-effectiveness of all systems, according to cost per 1000L, improves with higher oxygen demands. Cost per 1000L is particularly high for the Ultrox storage system at low admission rates; this system might only be appropriate for health centres with high annual patient loads, as the output

capacity is over-specified to meet low oxygen demands (not shown). In general, total annual costs increase with higher patient admission loads, but not linearly as shown in Figure 5. There seems to be a critical cross-over point around 3500 to 4000 patients per year; for all systems except the grid-charged system there is a step increase in annual cost. Increased costs are due to duplicate modules required to meet demand beyond this threshold. This is particularly apparent for the Scenario A Ultrox system beyond 4500 patients per year.

Figure 5: Annual cost for health centre sizes ranging from 500

to 6000 under-five patient admissions annually.

7 Discussion For the specific Gambian context analyzed, the energy storage systems are preferable, in terms of cost per 1000L and initial capital cost, for both levels of grid power availability. The Ultrox storage option with good power (10 hours) appears to be a more robust option, if consistent annual cost was a high priority for planning, but is sensitive to meeting high simultaneous demand at low power availability.

The solar-powered system is a very economical option, with long projected panel life-spans, low amortized costs, and minimal operating costs; however, solar panel efficiency drops significantly on cloudy or rainy days, or with dust build up, factors which were not accounted for by our model. This is an important consideration as rainy season leads to increased prevalence of respiratory illnesses and thus demand for oxygen [12]. Although the upfront capital cost is more than that of the grid system with comparable cost-effectiveness, reductions in solar technology costs continue to improve the economics of this option.

For all systems compared, capital equipment costs are the main contributor to overall system costs, as operating and maintenance costs tend to be minimal in comparison. Thus even though an advantage of storing oxygen is that oxygen losses due to under-utilization are reduced, the relatively high capital cost of the Ultrox system still limit any major cost-effectiveness advantage. This may not be true for all storage system configurations, however; for future work, we will analyze the Diamedica low-pressure reservoir storage option. With a much lower capital cost, the cost-effectiveness of this system might prove to be comparable to the energy storage options presented here.

3 3

These conclusions are limited to the oxygen and energy storage options analyzed specifically for the Gambian context presented. In general, our analysis indicates that the appropriate choice of oxygen supply system (energy or oxygen storage) in settings with poor power is dependent on child admission rates (and thus expected oxygen demand), hours of grid power per day, and whether or not the system is designed to accommodate for peak periods of simultaneous demand. More detailed comparisons of other configurations (i.e. the Diamedica system) are planned.

We acknowledge that there are limitations of this cost-effectiveness model. Cost estimates are dependent on the accuracy of the data used to build the model, and are subject to variability. The model also provides a strictly technical comparison of system cost-effectiveness. In other parallel work, we present a framework for comparison which encompasses criteria beyond cost-effectiveness, discussing the technical feasibility, sustainability, usability, and technical complexity of these system configurations in more detail [13]. All of these factors should be taken into account when choosing an oxygen system which compensates for poor power. Furthermore, the systems compared are too early in their design and development to make any assumptions about potential health benefit; however, it is important to keep in mind that cost-effectiveness is often linked to clinical benefit (e.g. cost per child treated, cost per disability-adjusted life-year (DALY) averted) [14], [15]. Lastly, the computer tool itself is not currently in a format that is easily shared and disseminated to interested users. Future work can involve developing a user-friendly interface such that the tool can be more widely accessible.

8 Conclusion In settings with intermittent power, solutions for medical oxygen supply that compensate for unreliable power are needed to minimize child pneumonia deaths. We developed a model, implemented as an Excel computer tool, that accounts for a wide range of configurable contextual input parameters to explore the question of whether it is more cost-effective to use a health centre’s limited amount of electricity per day to store energy or to generate and store oxygen for use when power is unavailable. Using this tool, we compared four oxygen storage systems to grid- and solar-charged energy storage options, specifically for The Gambia. The energy storage options were found to be less costly, however these conclusions are limited to the systems chosen for comparison. Our model is applicable to other oxygen and energy storage configurations, and comparisons of other systems are planned. In general, cost-effectiveness is sensitive to child admission rates, hours of grid power per day, and whether or not the system is designed to accommodate for high simultaneous oxygen demand.

Our model is a big advance on previous tools for comparing energy and oxygen storage configurations. These types of cost analyses are useful for understanding oxygen system economics on a broader level and for justifying further research and development into better oxygen systems for these contexts.

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