Advantages and Challenges of Composting Reactors for Household Use: Smart Reactor Concept

Abstract

In the European Union, 88 Mt of food waste is generated annually, accounting for 6% of total EU greenhouse gas emissions. To reduce the amount of bio-waste going into the landfills, the composting of bio-waste at a household level must be facilitated. Traditional composting devices for garden and household biological waste solely rely on natural processes and do not hold online process control features or require energy input. This study describes a design and construction of a smart composting reactor for improved composting process control and compares the developed system with other laboratory-scale reactors and commercial devices available for this purpose. The Alternative Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis method were used to assess the importance of various parameters and devices. The results showed good thermal insulation by reducing thermal transmittance from 1.87 W/m²K to 1.27 W/m²K, the effective sensor system performance of the constructed system, providing continuous data logging of temperature, moisture, and gas concentration levels. The system demonstrated 58% proximity to the ideal solution.

1. Introduction

In 2019, around 931 Mt of food waste has been generated globally, of which 61% (568 mt) was produced by households [1]. In that year, the European Union (EU) generated almost 225 Mt of municipal waste. This corresponds to 502 kg per person annually [2] and, from that, approximately 35% (88 Mt in total) contributes to food waste [3]. As estimated, food waste accounts for 8–10% of global greenhouse gas emissions [1] and about 6% of total EU greenhouse gas emissions [4], thus demonstrating the importance to revisit the current management strategies for bio-waste. Currently, to increase the recycling of bio-waste, the EU has set up the target to recycle 65% of municipal waste and to reduce the amount of biodegradable municipal waste going to landfills to 10% by 2035 [5]. Nevertheless, most of the municipal waste generated in the EU is still landfilled (24%) or incinerated (27%), with less than half recycled (31%) and composted (17%) [6].

The apparent trend in bio-waste recycling has also facilitated the introduction of bio-waste composting—biological decomposition of the waste to produce valuable nutrients for the soil under aerobic conditions. The produced material can be further reused as a soil enhancer or fertilizer [7] or combined with heat recovery [8] to yield the production of alternative energy sources. A compost heat recovery system in addition to carbon storage and land nutrition can provide an economic advantage in heat supply, especially to geothermal plants, reducing the cost from 0.120–0.124 €/kWh to 0.074–0.087 €/kWh [9]. Substantially, via composting, up to 50% less waste can be generated [10].

Composting is a complex biological process that depends not only on the microorganisms involved in the decomposition of organic matter and composted material structure but also on the physicochemical and thermal conditions that affect the activity of microorganisms [11]. Compost aeration should be well defined, otherwise it could create a detrimental effect. As determined by Lau et al., 0.2 L/min kg volatile matter (VM) aeration rate reached thermophilic temperature (55 °C) in 3–4 days; however, 1.0 L/min kg VM rate created a cooling effect, keeping the compost temperature at around 20 °C [12].

Most of the available composting solutions are produced for industrial or outdoor use [8,9] and are not suitable for in-house application. Moreover, since the process is lengthy and may not be suitable for everyday use due to the large amounts of biological waste required [13], in-house composting systems are currently being developed. The aim of this study was to create an affordable but efficient composting system, with specially selected sensors to monitor process quality, that is suitable for in-house use and which does not require excess energy input. Construction, design, and component selection were complemented with multi-criteria decision-making analysis for available composting solutions to evaluate the sustainability and public acceptance of the proposed concept.

2. Composting Reactors for Households

Traditionally, composting has been associated with simple windrows, passively [14] or forced aerated windrows [15], in-vessel composting [16], and vermicomposting [17]. From these, in-vessel systems are regarded as superior due to their compactness, better control, and heat-capture capacity [18]. Furthermore, composting in a reactor has a significantly higher organic matter decomposition rate (35.7%) within the first 7 days than in windrow composting (19.4%) or static heap (8.9%) [19]. At the same time, in-vessel systems are still not fully suitable for urban spaces, e.g., flats, though they could reduce the costs of waste collection and minimize disposed bio-waste volume. As estimated, the decentralized pre-composting of kitchen waste can reduce the mass by 33% and volume by 62%, with the production of mature compost in about 2.5 months [14]. Commercially available small compost systems (Table 1) typically do not have or possess only some smart features, or they are energy intensive. Furthermore, the compost quality from home production technologies is still risky due to the lack of data about compost pathogen content, quality, or chemical content [15]. Research-level reactors (<60 L working capacity) have been most often used only to study aerobic degradation, simulate the actual composting process, or study the impact of some specific physical (e.g., aeration) or biochemical parameters on the composting process [11,12,20,21] and do not target specifically indoor use. Thus, the production and assessment of an affordable indoor, in-vessel system that acquires both traditional composting features and smart monitoring tools are still essential for efficient bio-waste recycling.

The main basic variables that have been suggested for composting process monitoring include moisture content, aeration conditions or gas concentration, and temperature [22]. The monitoring of these parameters is essential to increase the reactor’s productivity, especially if this reactor will further employ any bioaugmentation with specially selected microorganisms. Physical variables, such as moisture level or temperature, have a critical influence on microbial activity. If water is allowed to leach and evaporate, the moisture level will not be sufficient for composting, and biological activity can decrease from 70% to below 40% in 30 days and to 10% in 50 days [18]. Otherwise, if the moisture level is too high, an anaerobic composting process will take place, and different gases, such as methane or ammonia, will be generated. For the best results, the moisture level must be between 50 and 75% [23]. Some smart composting systems are used for monitoring composting conditions only; however, some are fully autonomous, using sensors in feedback loops to optimize conditions, for example, by switching on/off ventilation, heating, or using a water pump if the temperature or moisture levels are out of optimal range [23,24], resulting in the need for additional energy input via electricity. To compare and assess the available technologies, the Alternative Hierarchy Process (AHP), Complex Proportional Assessment (COPRAS), VIKOR, MULTIMOORA, or a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method (MCDM) can be used. In general, multi-criteria analysis (MCA) is a comprehensive evaluation method that considers multiple factors that determine the quality of the provided solution [25]. MCA TOPSIS provides a model of possible scenarios as close as possible to real-life conditions, allowing an opportunity to assess otherwise incomparable alternatives. The solutions are evaluated in relation to the ideal scenario (ranking the solutions from 0 to 1, where 1 is the ideal variant and 0 is the anti-ideal variant) and result in relative proximity to the ideal solution for each evaluated alternative [26,27,28]. This complex problem assessment process requires a comprehensive analysis of available solutions; therefore, all possible factors must be considered, such as the effect on national economics, technical specifics and technology readiness, and the influence on society and environmental impact. An approach combining MCA AHP, TOPSIS, and sensitivity analysis was used within this study to assess the available composting technologies. The developed smart composting reactor technology was based on simple and basic parameter control (Figure 1) combined with the in-vessel composting principle.

Table 1. Operational parameter comparison for small-scale composting systems suitable for household use.

Origin	Name for TOPSIS MCDM Analysis	Work Volume, L	Composting Time, Days	Compost Pre-Treatment	Monitoring of Composting Process	Refs.
Reactors manufactured under laboratory conditions		21	100	Dry in the oven (65 °C)	Moisture (maintained between certain limits).	[29]
		60	21	Mix; adjust moisture content and exact C/N ratio	Temperature, CO2, ammonia (for 1 h measurements every day), taking samples each day for further analysis (bacterial populations, GI etc.).	[30]
		64	10	Without pre-treatment	Temperature, CO2.	[31]
		22	~13 (300 h)	All the materials mixed together	Tracking temperature and moisture at different heights, oxygen content, and leachate water. Measuring weight at time intervals.	[20]
	A—rapid composting device for households	20	10	Built-in grinder.	The composting device comprises a grinder, a plug-flow composting bin, a heater, a spiral stirrer, a deodorizer, a leachate collector, and an operation control system. The plug-flow composting bin comprised the initial, heating, thermophilic composting, and cooling composting (or post-composting) sections, operating at different temperatures. The deodorizer comprised a small fan, an ultraviolet light with a wavelength of 185 nm, and a duct. The operation control system included switch buttons, timers, software systems, and an operator interface.	[13]
	B—experimental continuous composting reactor	30	30	Four options: (a) crush in a garbage disposal unit; (b) mince with particle sizes of approximately 15 mm; (c) crush, heat at 70 °C and cook for 70 min; (d) crush and freeze for 12 h at −15 °C, then melt in an oven at 40 °C for 30 min.	By a high-resolution camera with night vision. The system can be monitored via the internet. The performed hourly data of pH and biogas analysis is recorded.	[32]
	C—experimental reactor with monitoring system	55	15	A special selection of materials for the study with specific proportions. All the materials mixed together	Temperature continuously. Moisture, weight loss, C:N ratio, H ion conc. weekly.	[12]
	D—experimental composting reactor;	32	14	Mixing	Temperature.	[33]
	E—reactor unit with optimized control system	9	60	Mixing	Temperature, airflow, CO2, O2.	[34]
	F—closed-loop, heat-composting experiment	68	70	Mixing	Moisture level, temperature, sample collection.	[35]
	G—small-scale composter used for indoor composting	15	30	Cutting the materials to 30–70 mm in size; add garden waste as a bulking agent (ratio 3:17)	Temperature.	[36]
Commercially available composting reactors	I—commercially available small-scale composter for households	20	0.3–3	Without pre-treatment	No monitoring systems.	[37]
	H—commercially available large-scale composter for households	40	0.5–1	Without pre-treatment	Temperature sensors and timer.	[38]
Smart Reactor System for Household Use	J—household composting device prototype	70	14–28	Without pre-treatment, however, thermal pre-treatment is recommended to shorten the composting time.	Gas, moisture and temperature sensors, monitoring process possible remotely.	This study

Table 1. Operational parameter comparison for small-scale composting systems suitable for household use.

Origin	Name for TOPSIS MCDM Analysis	Work Volume, L	Composting Time, Days	Compost Pre-Treatment	Monitoring of Composting Process	Refs.
Reactors manufactured under laboratory conditions		21	100	Dry in the oven (65 °C)	Moisture (maintained between certain limits).	[29]
		60	21	Mix; adjust moisture content and exact C/N ratio	Temperature, CO2, ammonia (for 1 h measurements every day), taking samples each day for further analysis (bacterial populations, GI etc.).	[30]
		64	10	Without pre-treatment	Temperature, CO2.	[31]
		22	~13 (300 h)	All the materials mixed together	Tracking temperature and moisture at different heights, oxygen content, and leachate water. Measuring weight at time intervals.	[20]
	A—rapid composting device for households	20	10	Built-in grinder.	The composting device comprises a grinder, a plug-flow composting bin, a heater, a spiral stirrer, a deodorizer, a leachate collector, and an operation control system. The plug-flow composting bin comprised the initial, heating, thermophilic composting, and cooling composting (or post-composting) sections, operating at different temperatures. The deodorizer comprised a small fan, an ultraviolet light with a wavelength of 185 nm, and a duct. The operation control system included switch buttons, timers, software systems, and an operator interface.	[13]
	B—experimental continuous composting reactor	30	30	Four options: (a) crush in a garbage disposal unit; (b) mince with particle sizes of approximately 15 mm; (c) crush, heat at 70 °C and cook for 70 min; (d) crush and freeze for 12 h at −15 °C, then melt in an oven at 40 °C for 30 min.	By a high-resolution camera with night vision. The system can be monitored via the internet. The performed hourly data of pH and biogas analysis is recorded.	[32]
	C—experimental reactor with monitoring system	55	15	A special selection of materials for the study with specific proportions. All the materials mixed together	Temperature continuously. Moisture, weight loss, C:N ratio, H ion conc. weekly.	[12]
	D—experimental composting reactor;	32	14	Mixing	Temperature.	[33]
	E—reactor unit with optimized control system	9	60	Mixing	Temperature, airflow, CO2, O2.	[34]
	F—closed-loop, heat-composting experiment	68	70	Mixing	Moisture level, temperature, sample collection.	[35]
	G—small-scale composter used for indoor composting	15	30	Cutting the materials to 30–70 mm in size; add garden waste as a bulking agent (ratio 3:17)	Temperature.	[36]
Commercially available composting reactors	I—commercially available small-scale composter for households	20	0.3–3	Without pre-treatment	No monitoring systems.	[37]
	H—commercially available large-scale composter for households	40	0.5–1	Without pre-treatment	Temperature sensors and timer.	[38]
Smart Reactor System for Household Use	J—household composting device prototype	70	14–28	Without pre-treatment, however, thermal pre-treatment is recommended to shorten the composting time.	Gas, moisture and temperature sensors, monitoring process possible remotely.	This study

3. Materials and Methods

3.1. Multi-Criteria, Decision-Making Analysis to Select Optimal Composting Solutions

To compare the available commercial and prototype-level technologies with the new smart composting system, TOPSIS MCDM was used in pre-defined stages (Figure 2).

To assess 14 pre-defined composting technologies, technological, environmental, and social indicators were defined (

Table 2. Indicators for TOPSIS analysis.

Factor Group	Indicators	Value	Best Alternative
Economic indicators	Product cost when available for consumer, EUR	EUR	MIN
	Market availability for a finished product	1 to 5	MAX
	Device energy consumption	kWh per day	MIN
Technological indicators	Device volume	liters	MAX
	Composting time	days	MIN
	Monitoring system for composting stability	1 to 5	MAX
	Pretreatment required outside the device	1 to 5	MIN
	Technological readiness and availability for production	1 to 5	MAX
Environmental indicators	Waste saved in landfills	kg per year	MAX
	Emission reduction	kg per year	MAX
	Environmental pollution generated in device production and operation	1 to 5	MIN
	Benefits to the environment from biomass utilization for this alternative	1 to 5	MAX
	Impact on climate change and GHG emissions	1 to 5	MAX
Social indicators	Availability to social groups	1 to 5	MAX

). The values for the comparison were derived from scientific literature and other reliable sources of information such as a manufacturer or experimental data. In some cases, calculations and assumptions, based on similar cases, were made to fully describe an alternative. In the case of an abundance of quantitative data or the need for qualitative evaluation, assessment from experts in engineering and natural sciences was used. Indicator weight in the primary decision matrix was determined by the AHP method using engineering expert judgements, using pre-developed AHP software for indicator priority calculations [39]. Sensitivity analysis was carried out later to evaluate the influence of indicator weight on the elasticity of alternatives. Sensitivity analysis was used to observe any changes in TOPSIS results in relation to the weights of each indicator. Within this study, the weight values were changed from 0.1 to 0.9 for the indicator tested and divided equally between the other 11 to maintain the weighted sum of 1. Equal variance t-test for 95% confidence was used to determine the significance of the difference between TOPSIS results for the technologies evaluated.

3.2. Smart Composting Reactor Monitoring System Construction

To create and operate a remote monitoring system for a smart composting device, a set of sensors was selected together with data transmitting and receiving modules. All were either installed or connected to the reactor (Section 3.3.) to collect and transmit information about the ongoing processes and evaluate the composting efficiency and predict further actions of the composting process. Furthermore, a data-receiving and -transmitting setup was produced.

3.2.1. Sensors

The optimal conditions of the composting process result in an initial phase temperature of around 10–42 °C, and up to 45–70 °C at the second phase; thus, the necessary temperature range is 10–70 °C, and the process may last from a few days to several weeks [40]. As the relative moisture might reach ~90% and the sensors are inserted inside the composting reactor, the temperature sensors must have dust and water protection of no less than IP53.

Temperature, moisture, and gas sensors were used as basic controllable parameters in the smart composting device development. All sensors were powered by a 5 V DC power supply to avoid DC/DC converters. This enabled direct connection to Arduino Nano board pins or power supply connectors.

Based on the investigation of market-available parts, the temperature sensor DS18B20 (Sonoff, China, IP68), with a temperature range from −55 to ~125 °C and ±0.5 °C accuracy, was selected. This sensor does not require external components (voltage amplifier, as in thermocouples), it is cheaper than RTDs, and has a 1-Wire connection, therefore multiple DS18B20 sensors can be connected to one digital pin. Moreover, DS18B20 has a low operating current, up to 1 mA, therefore it is good for energy-effective device development.

To measure compost moisture, two 5 V DC soil hygrometers, Joy-iT Soil Moisture sensor SEN-MOISTURE (SIMAC Electronics, Germany, current up to 35 mA) and capacitive SEN0193 DFRobot Gravity (current up to 5 mA), a corrosion-resistant analog soil moisture sensor, were selected. These sensors could determine the relative moisture in the full range of 0–100% based on compost resistance measurements. Two sensors were selected to exclude individual mistakes and for more accurate measurements [30,31].

For the control of the aerobic composting process and monitoring of unwanted gas production, the 5 V DC Semiconductor Sensor for Combustible Gas MQ-2 (WINSEN, China), with a range of CO₂ gas 200–10,000 ppm, was selected.

3.2.2. Development Board

The selected development board had to have enough storage capacity for the used software, enough connection pins to connect all the necessary peripherals, and operate using 5 V DC to properly power all connected devices. For this application, the Arduino Nano development board was selected.

A microcontroller unit (MCU) is a compact integrated circuit that can make specific operations defined by the user. The microcontroller used in this study consisted of a processor, memory, and some input and output (I/O) peripheral devices on one platform. The board had 22 digital pins, an ATmega328 microcontroller unit, and a 5 V USB power connection. The operating current of MCU can reach up to 19 mA and 40 mA for I/O pins, therefore all sensors can be directly connected to I/O pins for data and power. The ATmega328 has built-in 32 KB flash memory and a built-in bootloader and AVR architecture, therefore it can be easily programmed with different programs, such as Microchip Atmel Studio or Visual Studio. [41].

3.2.3. Data Transfer

For ease of use, remote data transferring was selected. To ensure more flexibility in the placement of the acquisition system part with data logging, it was defined that the data transmittance range had to be at least up to 50 m, with the ability to transmit the signal through the walls. Therefore, short range (up to ~10 m, e.g., Bluetooth and Li-Fi networks) was excluded. Although in a network of 2.4 GHz frequency Wi-Fi modules can transmit over a range of up to 350 m, and radio modules can work in a range of up to 800 m, such long-range solutions were excluded to ensure a stable operation in the continuous exploitation of the composting reactor and to avoid loss of data due to surrounding interferences. Thus, the NRF24L01 module (OKYSTAR, China), which can provide ~100% signal up to 100 m in the open air, was selected.

3.2.4. Data Acquisition Systems

The data acquisition part for data collection and processing was designed. All the data from the sensors were logged into MS Excel with real-time data presented in graphs to visually illustrate the changes in the measured parameters during operation. Such visualization was designed to ensure rapid response in case of any process errors.

The base station included the selected development board connected to the computer. To receive the data, the development board was equipped with the NRF24L01 radio module supplied with a 3.3 V DC radio modem and PA LNA amplifiers (Figure 3).

To power the NRF24L01 radio module to the Arduino Nano development board, an additional 3.3 V power adapter (OKYSTAR, China) was needed, as the standard 3.3 V pin on the Arduino does not provide enough current to power the NRF module. Therefore, the development board was connected to the computer through a 5 V USB combining a charging and data cable, which enabled data transfer and development board powering.

3.2.5. Data Transfer System

As a middle step between data collection and data logging, a data transmission part was designed. This part (Figure 4) included a development board, the NRF24L01 module with a 3.3 VDC radio modem (the same as in the data acquisition part), all the sensors, and a micro-USB power supply. There was no connection to any external devices (computer or other), and the sensor transmitter was working independently according to the code in MCU. To ensure the correct work of the temperature sensors, the data pin was connected to the power pin with a 4.7 kΩ pull-up resistor.

3.2.6. Power Supply and Electrical Part of the Smart Composting Reactor

A Micro-USB 5 V DC 2 A power adapter was used as a power supply. This AC/DC adapter converts 230 V AC to 5 V DC, and it was connected to power the Arduino Nano board directly through a USB port. The use of an AC/DC adapter ensures long-term operation, though it loses the flexibility to operate the device off-grid; in the future, independent power sources can be used. Transfer system current is around 100 mA during energy saving mode and 140 mA during data transfer; NRF modules are disabled in energy saving mode, therefore, most of the time, the device works with 0.50 W power.

3.2.7. Code Development for Data Receiving and Transferring Parts

MS Visual Studio software was used to write the program code for the Arduino Nano development board based on Atmel ATmega328 MCU. This program gives access to microcontroller peripherals and interfaces. Programs were written in assembly or in the C language using an MCU built-in bootloader that enables the loading of user programs without a separate device. The transfer system gathers data from sensors every 10 s and operates in power-saving mode in the meantime. The acquisition system part does not have any power restrictions. It is always in an active state, continuously waiting for the signal from the transfer system and ensuring continuous data flow while connected to the computer via USB. The receiving part uses a 9600 baud rate port to display gathered data and concurrently fill the MS Excel table every 10 s.

3.3. Reactor Construction and Operation

To test the developed monitoring system, an in-vessel compost reactor was designed and applied. The sensor data were collected and the system was analyzed for the measurement logging stability and reliability.

3.3.1. In-Vessel Compost Reactor

The composting reactor consisted of two barrels: 310 L Polypropylene (PP) vessel as an outer shell and 90 L mixed low- and high-density polyethylene (HDPE + LDPE) vessel as the composting reactor. The void between the inner and outer barrel was filled with polyurethane foam heat insulation (heat transfer coefficient λ = 0.035 W/(m²·K)). The inner reactor was detachable to ensure cleaning and compost extraction.

3.3.2. Placement of the Monitoring System

The sensors were fixed to the side of the compost reactor to avoid any damage during the compost mixing. Similarly, the data transmission system was fixed and placed above water level. Cable exit holes in the plastic container were fixed with a waterproof sealant to ensure moisture-proof conditions for the sensor system transfer part. Soil moisture sensor was placed in the bottom part of the sensor systems box, while gas sensors were fixed to the reactor’s inner-cap side. The temperature was measured at the heights of 5 cm and 20 cm from the bottom of the vessel (Figure 5).

The sensor system acquisition part was inserted into a cover so that only the USB connector was visible. Care was taken to minimize the used space, simultaneously retaining suitability to monitor parameters and to receive feedback from the controlled system through the human–machine interface [42].

3.3.3. Test Conditions

Three 36-h tests with water and one two-week test with compost were made to test the developed sensor system. The first water test was made to gain data on how quickly water will lose heat without insulation. The other two tests were performed with insulation to gain intel about insulation effectiveness. All tests were made with 40 L of warm water (t = 50 °C) as a starting point. The compost reactor was sealed with a cap, thus isolating the system. Gas collection and aeration systems were disabled in water tests. In the compost test, the vessel was filled with 35 L of pre-boiled cabbage cooled to ambient temperature (24 °C). The remaining 55 L of reactor space were left for air. All the measured parameters were collected at the same time and an aeration system was enabled to provide enough oxygen during the composting process. Air was delivered through a perforated circular rubber pipe and, afterwards, through a metal mesh in the bottom of the vessel for better air distribution. The designed air flow rate was 10 L/min for 1 min every hour.

4. Results and Discussion

4.1. Validation of the Smart Pilot Scale Compost Reactor

Water tests (Figure 6a) demonstrated the proper performance of the insulation and validated the performance of the constructed sensor system. The temperature graph formed smooth lines, indicating no interruptions in the logging process. An overall decrease of 20 °C in the non-insulation test was observed after 36 h of operation. In comparison, tests with installed insulation reduced the heat loss by around 5–10 °C. Furthermore, the temperature in the insulated reactor tests decreased gradually; however, without insulation, the temperature first decreased exponentially and then, when approaching room temperature, continued to decrease more linearly. While falling to 32 °C (the lowest temperature in the test with insulation), the heat loss rate in the uninsulated water test was approximately three times faster than the average of heat-insulated water tests during the experiment. Thermal transmittance (U) values were 1.87 W/m²K and 1.27 W/m²K for uninsulated and insulated tests, respectively. Due to the relatively small temperature differences between the water and room temperatures at the end of the test, water tests with higher initial temperatures can be performed to provide more accurate heat-insulation data, if needed. The relative moisture was mostly stable with a value of 92 ± 2%. However, it had some deviations from a straight line in reading values that might be caused by the physical sensor–medium interaction, as the heated water vaporizes and then condenses, potentially changing the submergence level of the sensor. This might also be an issue in real composting conditions, as biowaste compresses over time, which could lead to unreliable measurements.

In the food waste composting test (Figure 6b), the compost produced heat by itself, and the heat began rising for the first three days to reach 35 °C. Further, for the next 4 days, the temperature remained stable (~1–2 °C decrease). A slight decrease was observed at the later experimental stage; however, it was still 3–4 °C higher than the ambient temperature. Based on the results, it can be concluded that, after 7 days, new food waste must be inserted to sustain active heat production. Moreover, aeration periods can be adjusted for better results. Average moisture was 20–25%, therefore compost may be watered for better temperature results. Gas parameters showed that gases in the compost reactor overdue ambient parameters 3 times and mainly were related to unpleasant odors. Despite this, the adjustment of the aeration level during composting could improve compost maturity, reactor supplementation with odor-minimizing substances or microorganisms [43], and could be more relevant to a household-level composting reactor. In the future, the sensor set and the device power part can be modified and supplemented for specific needs [32,37], e.g., for the monitoring of odor appearance. In future improvements, the monitoring system can be improved to detect various gases that affect the composting process to identify the most favorable environment for the reproduction of bacteria and fungi. The addition of VOC, H₂S, and NH₃ sensors could provide additional information about odor-causing gasses, while the currently installed combustible gas sensor gives information about the generation of methane that is crucial for minimizing safety risks and avoiding anaerobic digestion. To gain a better insight into the possible correlation between parameters, it can be recommended to visualize temperature and gas concentration data in one output graph; therefore, data logging of all measured parameters should be performed with a relatively similar sampling frequency. Furthermore, long-term composting tests with different raw materials are necessary to test the improved system throughout all stages of the composting process.

4.2. MCDM Results to Select Optimal Composting Solutions

Weight calculations using the AHP method were performed for 14 socioeconomic sustainability indicators. The experts concluded that Technological indicators were of higher importance than others (14%), followed by Energy Consumption (12%) and Pretreatment Outside the Device (11%). Economic and Social indicators each represent a fraction of less than 5% of the total weight of indicators. Using AHP predetermined indicator weights, an assessment of the identified (Table 1) technologies by TOPSIS was made (Figure 7).

The best technologies after TOPSIS analysis were determined to be the commercially available large-scale composter (H), the smart composting system developed within this study (J), and four different prototypes from research articles with similar TOPSIS values (C, G, A, D). TOPSIS results for this evaluation were similar and ranged from 0.37 to 0.58, giving all alternatives an average level of compliance with the ideal solution. T-values for the TOPSIS results suggest that the determined difference between technologies represents a true difference. Sensitivity analysis (Figure 8, Figure 9 and Figure 10) was carried out to the selected best alternatives to evaluate the influence of change in importance and indicator weight values on the TOPSIS-determined results.

Changing the significance of Product Cost (Figure 8) increases the relative proximity to the ideal solution for technologies G and C that have lower market prices. Sensitivity analysis shows that technologies D, F, I, and J are stable; however, relative proximity is only around 0.4; therefore, in the development process of new technologies, price should be lowered as much as possible. Technology developed within this study lacks market potential due to the proposed price. Device Energy Consumption sensitivity analysis highlighted commercially available devices as energy inefficient—minimized composting time and maximized productivity results in higher energy consumption and relative proximity to ideal solution decreases to <0.1. Within this study, two commercially available devices were described, and both have little or no thermal insulation, which results in energy losses and higher energy consumption. The experimental device developed in this study reached the best results in sensitivity analysis, exceeding the 0.9 mark. The increased significance of Market Availability results in an increase in commercially available device-relative proximity (>0.8 at 0.9 weight). Other technologies reviewed within this study are in a prototype phase and are thus not ready for commercialization. Prototype devices that are not aimed at scientific studies but are consumer-targeted are more stable in the sensitivity analysis. Furthermore, scientific devices are too complex and lack market potential.

The sensitivity analysis of technological factors (Figure 9) allowed for the determination of the optimal Composting Device Volume (30 to 45 L). Devices B, D, and H were stable within this volume and changed the relative proximity by <0.1. The optimal Composting Time was determined to be under 30 days for the device to be stable when the weight of the Composting Time indicator changed. If the composting time exceeds 30 days, relative proximity decreases to <0.3. Technologies C, F, and J were stable when changing the significance of the Monitoring System indicator, and the technology developed in this study showed the best relative proximity results (around 0.5 to 0.6). However, it should be noted that, currently, the Composting Time for the system developed within this study is theoretical and needs empirical confirmation. Required Pretreatment Outside the Device influenced commercially available devices positively (relative proximity 0.7 to 0.9 for alternative H) and scientific ones mostly negatively (relative proximity < 0.1 for alternative D). Commercially available devices have built-in pretreatment options; therefore, in the development of new technologies, integrated pretreatment systems should be made. Changing the significance of Technological Readiness and Availability for Production increased the commercially available technology’s relative proximity to >0.8 at the 0.9 mark. Commercially available technologies are fully developed, with minor improvements from time to time. Technologically ready prototypes that had increased relative proximity were determined to be A, E, and G. The device developed in this study lacks technological readiness and therefore must be improved.

Waste Saved in Landfills and Annual Emission Reduction (Figure 10) impacted reviewed device stability similarly. Technology H, which is a commercially available composting reactor, reached relative proximity close to 1 at the 0.9 mark. The proximity of other technologies decreased when the significance of indicators was increased. Technology H has the largest volume and shortest composting time of all reviewed technologies, which serves as a handicap in the environmental evaluation. Of all other technologies, device J has the highest proximity result. However, this evaluation does not include emissions or waste produced while using the device. Similarly, technology H shows the best results in an environmental pollution sensitivity analysis. In this evaluation, devices C, D, F, and J were determined to be stable. Technologies B, D, I, and J were determined to be stable when changing the significance of the indicator “Benefits to the environment from biomass utilization for this alternative”. From these technologies, device J showed the best proximity results (>0.5). This is due to lower energy consumption, higher emission, and waste reduction and better thermal insulation for energy efficiency. Climate impact sensitivity analysis concluded that alternatives E, F, and H are stable and have a relative proximity increase.

The more complex technologies (A to G) have a decrease in relative proximity in the social indicator sensitivity analysis (Figure 11) due to being unavailable and hard to understand for all social groups. Increased price and a change in volume impacted the relative proximity results; thus, more complex and expensive technologies would lack market potential.

Most of the evaluated composting solutions were research-level devices, produced for laboratory testing and composting process modeling. Therefore, technological factor sensitivity analysis suggests that research article prototypes are more stable than commercially available ones. Economic, environmental, and social aspects decrease the TOPSIS value for these alternatives. Unfortunately, most of the evaluated devices cannot be successfully marketed and sold to the consumer due to the equipment prices or inconvenient device specifications—working volume, energy consumption, etc. The most stable alternatives were determined to be the commercially available large-scale composter and the household composting device prototype developed in this study. Both solutions were developed to meet the needs of potential customers; thus, higher stability results than for experimental laboratory devices were expected. The results of TOPSIS and the sensitivity analysis for the smart composting system developed within this study allow for a comparison of the solution to commercially available and other research devices and highlight flaws and possible improvements.

Although, as determined by TOPSIS, the developed prototype is closer to the ideal solution than most of the compared research-level composting systems, the overall mark is still low (58% from the ideal solution reference). The improvements need to be performed to ensure ease of use by eliminating pre-treatment requirements (assuming indicator weight (Weight) 0.9, proximity to ideal solution (Prox.) is only ~60%) and providing a completely odor-free and easy-to-clean device. The implemented monitoring system accounts for Prox.~63% (Weight 0.9), which is exceeded by a reactor unit with an optimized control system (Prox. close to 1); thus, some improvements in the monitoring are needed. Market Availability and Composting Time criteria also show relatively high values, close to 100% and 80%, respectively (at a Weight of 0.9).

Global energy demand, together with sustainable waste management, is becoming more and more topical. Thus, the production of new sustainable technologies for waste minimization is still needed. Traditional aerobic digestion systems are limited to outdoor use or high volumes with no or a low possibility to monitor the composting efficiency. Within this study, we demonstrated the development of a smart composting system that has both parameter control and an insulated in-vessel format to be applicable for indoor use. The system maintains temperature for sufficient time and has simple exploitation requirements. Furthermore, the TOPSIS MCDM assessment demonstrated a comparable quality of the designed system to commercially available and scientific-level pilot systems.

5. Conclusions

A smart in-house composting reactor system was constructed. The distance-controlled monitoring system showed stable operation in all high humidity tests with water as well as in a 14-day approbation test with food waste. The temperature and relative humidity sensors provided uninterrupted data logging for the whole testing period. A 5–10 °C lower temperature and decreased thermal transmittance by 32% was detected in the reactor when compared to traditional cooling of a non-isolated system. The Alternative Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis demonstrated 58% proximity to the ideal solution of the constructed system. Nevertheless, further confirmation of AHP and TOPSIS results is needed for various composting materials to fully validate the obtained outcomes of this study.