A new approach using an open-source low cost system for monitoring and controlling biogas production from dairy wastewater

Journal of Cleaner Production 241 (2019) 118284

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A new approach using an open-source low cost system for monitoring and controlling biogas production from dairy wastewater Ianny Andrade Cruz a, Luciano de Melo b, Ariadne Nunes Leite a, dia Hortense Torres c, ~o Victor Melquiades Sa tiro a, Larissa Renata Santos Andrade a, Na Joa d e Rebeca Yndira Cabrera Padilla , Ram N. Bharagava , Renan Figueiredo Tavares a, c, Luiz Fernando Romanholo Ferreira a, c, * ^ndia, 49032-490, Aracaju, SE, Brazil Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farola ~o Caf ^ncia, SE, Brazil Federal Institute of Sergipe, Rod. Joa e Filho, 264, 49200-000, Esta c ^ndia, 49032-490, Aracaju, SE, Brazil Institute of Technology and Research. Av. Murilo Dantas, 300, Farola d Federal University of Matogrosso do Sul, Av. Senador Filinto Müller, 1555, 79074-460, Campo Grande, MS, Brazil e Laboratory for Bioremediation and Metagenomics Research (LBMR), Department of Microbiology (DM), Babasaheb Bhimrao Ambedkar University (A Central University), Vidya Vihar, Raebareli Road, Lucknow, 226 025, Uttar Pradesh, India a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 March 2019 Received in revised form 26 June 2019 Accepted 3 September 2019 Available online 6 September 2019

Biogas plants can effectively treat various sources of organic wastes and recover energy from biomass. However, anaerobic digestion (AD) is highly sensitive to process disturbances, which can affect the biogas production efficiency. Online process monitoring/controlling can be used to ensure efficient and stable biogas production, but automated instruments are still associated with high costs. Therefore, this study developed a low-cost system, using the open-source platform Arduino and its components, to monitor/ control some AD variables. To test the developed system (DS), some AD variables of the dairy wastewater inoculated with sewage sludge digestion was monitored/controlled in the liquid phase (pH, temperature) and in the gas phase (pressure, methane yield, biogas volume). The batch experiment was conducted in mesophilic conditions (38
C), with an inoculum/substrate ratio of 1:2 for 21 days, and it was also evaluated in terms of volatile solids (VS) and chemical oxygen demand (COD) removal. The DS maintained the desired pH and temperature conditions and informed an average cumulative biogas and methane concentration of 675.2 mL and 51.46%, respectively. The VS and COD removal rate obtained after the digestion was 45.35% and 80.1%, respectively. At the end of AD, an ecotoxicity test using Lactuca sativa seeds was performed and high digestate concentrations exhibited toxicity. Finally, the apparatus construction was feasible for the proposed work, indicating its economic potential viability. © 2019 Elsevier Ltd. All rights reserved.

handling editor; Mingzhou Jin Keywords: Anaerobic digestion Biogas production Process monitoring/controlling Low-cost system Platform arduino Phytotoxicity

1. Introduction Anaerobic digestion (AD) has become an attractive alternative for energy production because of its environmental advantages (Grando et al., 2017). During AD, various types of biomass and organic wastes are converted into biogas (60%e70% methane, 30%e 40% carbon dioxide, and traces of others gases such as hydrogen and ammonia), leaving a rich material that can be used as a high

* Corresponding author. Institute of Technology and Research. Av. Murilo Dantas, ^ndia, 49032-490, Aracaju, SE, Brazil. 300, Farola E-mail addresses: [email protected], [email protected], [email protected] (L.F. Romanholo Ferreira). https://doi.org/10.1016/j.jclepro.2019.118284 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

quality fertilizer (Deng et al., 2017; Xu et al., 2018). Furthermore, AD process can also minimize deforestation for fire in developing countries, take organic wastes away from landfills and replace part of fossil fuels, hence, reducing greenhouse gases emissions and odors (Nguyen e Khanal, 2018). Nevertheless, AD is a complex process due to the involvement of diverse microbial communities supporting biochemical reactions (hydrolysis, acidogenesis, acetogenesis, methanogenesis) and it may suffer from instability (Nguyen et al., 2015). Then, it is indispensable monitoring the AD for improving its stability and efficiency (Boe et al., 2010; Li et al., 2018), as well as developing costeffective online monitoring methods (Wu et al., 2019). Parameters like pH, volatile fatty acids (VFA) concentrations,

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biogas composition, biogas yield, and alkalinity are usually used as AD indicators (Jin et al., 2017; Lützhøft et al., 2014). There are several off-line methods for monitoring AD both liquid and gas phases such as gas chromatography (GC) (Ward et al., 2011), pH titration (Lützhøft et al., 2014), high performance liquid chromatography (HPLC) and mid-infrared spectroscopy (Falk et al., 2015). However, these methods are expensive, time consuming and commonly tested manually. There are also some studies of online monitoring systems based on the above-named methods (Boe e Angelidaki, 2012), but they usually require complex equipment and meticulous maintenance, or even a challenging pretreatment. Biolectrochemical systems have become an alternative for the online monitoring of these processes (Jin et al., 2017), but the development of efficient and cost-effective biosensors still needs improvements. Yu et al. (2016) state that the gas-liquid phase monitoring makes possible indicating the digester state by simple parameters such as the liquid-phase pH and biogas flow rate of the gas-phase, so they developed a system which combined biogas-pH monitoring and controlling thresholds. While Bernardi et al. (2017) developed a medium scale plant which monitor in real time the system temperature, pH, biogas and subsequently the methane through gas chromatograph. The above studies used programmable logic controllers (PLC), which is more expensive and usually used in industries, but it is possible to efficiently monitor the AD process using low-cost and accessible electronics and the platform Arduino. The Arduino is an open-source electronic platform which allows the building of scientific instruments, using the open-source hardware and software, and reducing the cost of researches (Maraba e Bulur, 2017). It has been efficiently used to monitor and control experimental equipment in automation processes by the scientific community. Among the various studies that have used it, Mesas-Carrascosa et al. (2015) performed the temperature and relative humidity of the air monitoring in agriculture, Resende et al. (2017) constructed a low-cost plant for biodiesel production, Rosa et al. (2017) developed an apparatus that controls an electrochemical pretreatment, and Ahmed et al. (2017) developed a system to measure only the methane production in the AD process, which is different of the present study that monitors more variables, such as pressure, biogas volume, pH and temperature. Therefore, one of the shortcomings of AD technologies is the high capital cost associated with reactors which are usually made of expensive materials (Budzianowski, 2016). Besides studies state that process automation and control should be integrated as part of the bioreactor design (Khanal et al., 2017), instrumentation (in both hardware and software aspects) is still associated with high costs (Maraba e Bulur, 2017). Thus, this study used the platform Arduino and some low-cost electronic components, to construct an automated system capable of online monitoring/controlling some indicators of the AD process in the liquid phase (pH, temperature) and in the gas phase (pressure, methane yield, biogas volume). Furthermore, the AD of dairy wastewater inoculated with sewage sludge was performed, and at the end of the digestion period, a toxicity assessment of the digestate was realized using the bioindicator Lactuca sativa.

heater, that goes on/off whenever temperature exceeds the set thresholds, holding the vessel temperature in the desired range. And the reactor mixing is accomplished by a system of magnetic stirring which offers a mixing velocity of 100e1500 rpm and is manually adjustable. The digester is also provided by a temperature sensor (DS18B20) that control the heater functioning and a suitable pH probe (Arduo eletro) with a transducer for Arduino that control the peristaltic pumps to add an acid or basic solution. Pressure is checkable due to a differential pressure sensor (MPX5050) at the biogas output line. Moreover, the software is provided by a series of biogas releases and delays in order to prevent critical overpressure or under pressure. Then, the produced biogas passes by a non-dispersive infrared sensor (CH4/GP) that enable the methane concentration measurement. Finally, the biogas goes to a receptor vessel which is above a load sensor (AF) sensible for strain gauge that informs the produced biogas volume. Despite the temperature sensor has been already calibrated in its fabrication, the other sensors (pH, pressure, load and methane) were calibrated in this research. Some standard solutions with known pH were used to calibrate the pH sensor and obtain its response values. To obtain the response values of the pressure and load sensor, different pressures were applied and monitored by a manometer, and different standard weights were used, respectively. Finally, to calibrate the methane sensor, a sample of CH4 standard gas (99,98%) was injected into the sensor, and its response values obtained. The obtained response values for each sensor were used to construct scatterplots, and then, obtain equations of the type y ¼ ax þ b (linear regression). Statistically, for each curve, a discrepancy of 5% represented by the variation coefficient was accepted, above that the values were discarded, and the procedure redone. Thus, all equations were added in the software and uploaded in the Arduino, so all the values obtained by the sensors are presented in their respective units. Some Arduino-compatible modular electronics known as Nanoshields (Circuitar) are also used, because they make the system more robust. The ADC 4e20 Nanoshield, for example, allows to measure the methane sensor with 4 mAe20 mA output with high precision and resolution. The Load Cell Nanoshield is connected to the load sensor and permits high-precision and high-resolution load cell measurement. With the MicroSD Nanoshield is possible to record all the data obtained in a microSD card, and with the RTCMem Nanoshield a real time clock is added to the system. The system software is written in the Arduino Integrated Development Environment (IDE) and is responsible for incorporating multifunction of equipment automation control, data acquisition, recording and display. In addition, a liquid crystal display (LCD), some light-emitting diodes (LEDs) and a sound system are also implemented to improve the user experience. The system is also equipped with a USB communication interface that provides the data export process to a Personal Computer (PC). The schematic diagram of the reactor used in this study is shown in Fig. 1. During the process, effluents samples can be collected, as well as at the end of the process, the vessel drain can be effectuated by means of a manual valve present in the bottom.

2. Materials and methods 2.2. Functioning 2.1. Apparatus construction This plant is composed of a Mariotte vessel having a capacity of about 2 L (working volume), closed with rubber stoppers in which five holes were open, in order to attach the pH and temperature sensors, to allow biogas output and to add an acid or basic solution (pH control). Heating is ensured by a thermostatically regulated

As previously mentioned, temperature and pH are the main parameters that control the prototype automatism. So, feeding runs if the pH value goes beyond the set thresholds, loading so, alkaline feed or acid one according to the registered pH increase or decrease. Analogously, substrate heating is controlled by a temperature probe, then, if substrate temperature goes bellow the set value the

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Table 2 Characterization of the inoculum used in the experiments. Analysis

Inoculum

Total solids (mg/L) Volatile solids (mg/L) Chemical Oxygen Demand (mg/L) pH value Temperature (oC)

915 270 1985.5 6.92 30

2.4. Characterization methods

Fig. 1. Flow and logic diagram of the experimental setup.

thermostats run turning the heating on, otherwise, they go off until the vessel cooling. The produced biogas is determined by means of volume displacement (Moukazis et al., 2017), done automatically using a bottle containing a liquid above the load sensor. So, as far as the biogas is produced, it goes to a receptor vessel that is full of a liquid and expel it; the load sensor measures the expelled liquid mass and thanks to the density equation saved in the firmware, the system presents the expelled liquid volume which is the same of the biogas produced. Methane quantity included in the biogas is measured, through a quantitative analysis, due to the CH4 sensor that informs its concentration in percentage. All the monitoring data is collected by the Arduino, then recorded in a microSD card and showed in a liquid crystal display (LCD) in real-time. The process record can be extracted to the PC at any time.

2.3. Raw material selection and inoculation In this study it was used a dairy wastewater (DW) from a cheese factory located in Campo do Brito/SE (10º440 38.000 S 37º290 42.300 W) which uses traditional technologies for cheese manufacture. The DW characteristics are shown in Table 1. For the anaerobic digestion process startup, anaerobic sludge (AS) from a municipal wastewater treatment plant located in Barra dos Coqueiros/SE (10º530 58.800 S 37º01057.500 W) has been used. This inoculum was collected, sieved (<5 mm) to remove larger particles and stored at 4
C in a cold chamber until its use. The properties of the inoculum used for this study are presented in Table 2.

Table 1 Main characteristics of dairy wastewater. Analysis

Units

Values

pH Total solids (TS) Volatile solids (VS) Chemical Oxygen Demand (COD) Biochemical Oxygen Demand (BOD) Oil and grease Total nitrogen Total phosphorus Sulfates Potassium

e (mg/L) (mg/L) (mgO2/L) (mgO2/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

4.48 52 640 46 465 78 057.3 42 650 820 1200 369.9 276 4.56

The characterization of the feed, the reactive mixture and the biogas were performed according to the following methods: total solids (TS), volatile solids (VS), total chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN) and fat content were determined in accordance with the Standard Methods for the Examination of Water and Wastewater (Apha, 2012). Temperature and pH were measured offline using a pH meter Spenccer and online using a sensor (DS18B20) and an electrode (Arduo Eletro), respectively. Ecotoxicological bioassays were carried out according to methodology proposed by Vilar et al. (2018). 2.4.1. Ecotoxicological assays using Lactuca sativa In order to assess the toxicological response, lettuce seeds (Lactuca sativa) were used as toxicity level bioindicators. The biotests applied in this paper were developed according to the methodology proposed by Vilar et al. (2018). Three replicates were tested on a set of dilutions (12.5%, 25%, 50%, 75%, 100%, containing 20 lettuce seeds each) using the raw dairy wastewater and the digestate (after the anaerobic digestion) samples. Negative control with 0.2 M NaCl solution and a positive control with distilled water were used. Using Petri plates, the lettuce seeds were put on a layer of filtration-type paper containing solutions (samples or distilled water) and properly covered by aluminum foil to maintain it in dark conditions. Then, they were germinated inside an incubator at constant temperature of 22 ± 2
C. The germination was registered after 120 h of exposure and germination rate (GR) calculated. The GR% is determined using the Equation (1), where SGA is the number of seeds germinated in the sample and SGC is the number of seeds germinated in the control.

%GR ¼

N
SGA *100 N
SGC

(1)

2.5. Experimental procedure Prior to incubation, the reaction vase was flushed with nitrogen to ensure an anaerobic ambient. Then, the inoculum and substrate were mixed to the required ratio of 1:2 (Koch et al., 2017) and inoculated in the reactor manually. It operated at mesophilic temperature conditions (approximately 38
C) with a hydraulic retention time of about 20 days (Kumari et al., 2018), with agitation set at 300 rpm. The pH was automatically adjusted to 6.5e7.8 by adding solutions of 1M NaOH and 4M HCl through peristaltic pumps, as ideal condition for methanogenic bacteria (Choong et al., 2017). Besides that, effluent samples were collected twice a week during the experiments for analyzing pH, COD, TS and VS. 3. Results and discussion 3.1. Equipment monitoring To evaluate the DS economic viability, it was made a quotation

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It is worth to note that the CB may have a higher efficiency, but for academic purposes the DS shows a better economic viability.

request of a commercial bioreactor (CB) with same functionalities, such as a set of peristaltic pumps, a measurement and actuation device, a reaction vessel and its support, temperature controlling, an agitation mechanism, connectors and a control software. The final value of it was approximately 25 000.00 US dollars, but it is necessary to point out some additional characteristics of the CB as its vessel capacity of 7 L, an aeration module, and that is made of glass and stainless steel. Nevertheless, to add a biogas (CH4, CO2, O2 and H2S) analyzer module in the CB, its price would increase to approximately 34 000 US dollars. Nonetheless, the purchase of all components cited in Fig. 1, and the DS total construction had an average cost of 1560.00 US dollars.

3.1.1. pH pH is a very important parameter in the anaerobic digestion because it shows the process stability and performance (Kumari et al., 2018). The anaerobic digestion process includes four steps namely hydrolysis, acidogenesis, acetogenesis and methanogenesis and the bacteria present in these stages largely depend on system pH where anaerobic digestion occurs (Mao et al., 2015). The ideal pH range in the AD process is between 6.5 and 7.8 which is sufficient for the survival of methanogenic bacteria and

(a) 8.00 7.00 6.00

pH

5.00 4.00 3.00 Online method (DS) 2.00

Offline method

1.00 0.00 0

5

10

15

20

Time, days

(b) 40.0

Temperature, oC

39.0

38.0

37.0 Online method (DS) Offline method 36.0 0

5

10

15

20

Time, days Fig. 2. pH (a) and temperature (b) variation of the effluent during the digestion period (Mean values of three independent replications; vertical bars represent standard errors).

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organic matter degradation (Choong et al., 2017). However, due to the dairy wastewater acidic and oily nature which resulted in more acid formation, the pH was initially in the range of 5e6, how Fig. 2 (a) shows. Aiming to avoid a pH larger decrease, 1M NaOH has been used as the alkali solution utilized for stabilizing the pH value just as carried out by Streitwieser (2017). In their study, Kumari et al. (2018) evaluated the co-digestion of dairy wastewater with cow manure, which had an initial pH of 6.86, indicating that its pH was sufficient for bacteria survival, and inhibition by acidification was not a problem. Sivakumar et al. (2012), had to adjust the influent (spoiled milk) pH by adding aqueous solutions of sodium bicarbonate, and notticed that lower pH resulted in lower volume biogas production, which indicates the need of the pH controlling. Observing Fig. 2 (a), it is also possible to see that up to day 11 the pH was between 5 and 6, which can be interpreted as the AD initial stages (hydrolysis, acidogenesis, acetogenesis). However, from day 12 the pH started to increase, reaching the ideal range for methanogenic bacteria and, as can be seen in Fig. 4, it is during this period that methane production actually occurred, thus indicating the process methanogenic phase. Every time samples were collected from the reactor, pH of them was measured in a common pH meter (Spenccer) with intention of comparing these values with those obtained by the DS, in order to demonstrate the equipment efficiency. In Fig. 2 (a) it is also possible to see the comparison between these values which were noticeably close. 3.1.2. Temperature AD processes can be accomplished over three temperature ranges: psychrophilic (10e30
C), mesophilic (20e50
C) and thermophilic (35e75
C) (Leite et al., 2017). The treatment becomes more difficult as temperature drops below 20
C (Bowen et al., 2014), so mesophilic and thermophilic are preferred in most anaerobic experiments, once higher temperature may lead to a higher biogas production (Leung e Wang, 2016). However, thermophilic AD has some disadvantages such as decreased stability, larger investments and higher net energy inputs, what makes the mesophilic digestors more common, mainly in tropical regions (Mao et al., 2015). The optimum temperature of digestion can vary depending on the effluent composition, but it is important to maintain it at an approximately constant level for achieving a maximum gas production rate (Kothari et al., 2014). Therefore, it is necessary to control the temperature during the AD process, and the built

5

system was able to do it as Fig. 2 (b) shows. The temperature of aliquots collected in the reactor was also measured outside the system and the results are shown in Fig. 2 (b) just like happened for pH, the values compared were noticeably close for temperature. However, it is important to note that the environment can influence in the measurement results, making the system results more reliable. 3.1.3. Biogas and methane yield The cumulative and daily biogas yield are shown in Fig. 3. On the first day, one peak of daily biogas yield (1.378 mL/day) was observed, due to the dairy wastewater higher COD content that could be quickly biodegraded in the first day. However, biogas production rate dropped after this peak, and no biogas was produced from day 3e12, indicating an apparent severe inhibition. This biogas production inhibition was likely caused by the substrate high digestibility that led to volatile fatty acids (VFA) overproduction and inhibited the methanogenesis process (Abudi et al., 2016). After 9 days of methanogenic bacteria self-recovery, the biogas production started again. From Fig. 3, it also can be seen that at the end of the digestion process, the total cumulative biogas yield was 675.2 mL. In Fig. 4 can be noticed that the methane production started after the 14th day, with the highest peak of methane content (51.46%) at the 21st day of digestion. In their study, Lovato et al. (2016) got 79.1% of methane content in the co-digestion of synthetic cheese whey and glycerin, while the present work just used the real dairy wastewater without glycerin addition, which could lead to a higher biogas production. 3.2. COD removal during AD process COD indicates the measure of organic pollutants present in the sample (Kumari et al., 2018). COD concentration with respect to time is shown in Fig. 5. The decrease in the COD concentration during the anaerobic digestion was observed, and it represents a decrease in the dairy wastewater organic pollutant. At the end of 21 days digestion period, the process obtained a COD removal efficiency of 80.1%. In their studies, Kumari et al. (2018) obtained a COD removal of 86.56% after the co-digestion of dairy wastewater and cow manure at mesophilic temperature, and Lovato et al. (2016) achieved 89% of COD removal during the co-digestion of synthetic cheese whey and glycerin. It is worth noting that in study carried out by Kumari et al. (2018) an inoculum composed of bovine

2.5 Cumula ve

700

Daily

2

600 500

1.5

400 1

300 200

0.5

100 0

0 0

5

10

15

20

Time, days Fig. 3. Cumulative and daily yields during anaerobic digestion of dairy wastewater.

Daily biogas yield, mL/day

Cumula ve biogas yield, mL

800

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Methane concentra on, %

80.00 % CH4

70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 0

5

10

15

20

Time, days

COD concentra on, mg/L

Fig. 4. Biogas methane concentration obtained by the built system using the NDIR sensor (Mean values of three independent replications; vertical bars represent standard errors).

50,000 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0

COD

0

5

10

15

20

25

Time, days Fig. 5. COD concentration with time for the effluent (dairy wastewater þ anaerobic sludge) coming out of the reactor (Mean values of three independent replications; vertical bars represent standard errors).

manure was used, which contains a different microbial diversity than the used in the present work, which modifies the process efficiency of COD removal. While Lovato et al. (2016) worked with a synthetic cheese whey plus glycerin, characterized by lower COD (5000 mgCOD/L) when compared with the real effluent, which could explain the greater COD reduction.

they obtained a VS removal of 17.4% and 16%, respectively, which indicates an increase in efficiency when the co-digestion is performed, what did not happen in the present work which used just one substrate (mono-digestion).

3.4. Toxicity 3.3. Volatile solids removal The reduction in volatile solids (VS) is one of the most valuable parameters for evaluating the AD efficiency and it indicates the substrate biodegradability (Abudi et al., 2016; Leung e Wang, 2016). As shown in Fig. 6 the VS concentration had a decrease and at the end of the process 45.35% VS removal has been reached through the mono-digestion of dairy wastewater. Pavi et al. (2017) reported 54.6% as the highest rate of VS removal in the co-digestion of organic fraction of municipal solid waste (OFMSW) and fruit and vegetable waste (FVM), but in mono-digestion of OFMSW and FVW,

Besides biogas, the AD also has as product a residue called digestate which is a mixture of partially degraded organic matter, inorganic compounds and microbial biomass (Alburquerque et al., 2012). This digestate can be used as biofertilizer in agriculture, but its direct application may be limited because it may contain heavy metals, infrequent parasites and seeds or weeds (Stefaniuk et al., 2016). So, ecotoxicity analyses of digestate before its use in agriculture can predict its environmental impact or a necessity for additional treatments (Tigini et al., 2016). Fig. 7 shows the results for the ecotoxicological test made using Lactuca sativa seeds. According to a study from Cesaro et al. (2015), analyzing organic

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35,000

Vola e Solids, mg/L

30,000 25,000 20,000 15,000 10,000 VS 5,000 0 0

5

10

15

20

25

Time, days Fig. 6. Volatile solids (VS) concentration with time for the effluent (dairy wastewater þ anaerobic sludge) coming out of the reactor (Mean values of three independent replications; vertical bars represent standard errors).

(a)

(c)

(b)

(d)

Fig. 7. Dairy wastewater germination index (a) and relative root growth (b). Initial mixture (dairy wastewater þ sewage sludge) and digestate germination index (c) and relative root growth (d).

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residues phytotoxicity using L. sativa seeds, all results for a germination index (GI) below 40% indicate seed inhibition sensibility, the values between 80 and 120% are not considered significant, and the values above 120% are considered as growing stimulus. Therefore, the results from this study indicate no toxicity for the dairy wastewater in natura, as can be seen in Fig. 7 (a), while Fig. 7 (b) shows a slightly growth (10% higher) when compared to the positive control, which evidences the wastewater performance as inducer in the root growth. In Fig. 7 (c), no germination was observed for the initial mixture (dairy wastewater þ sewage sludge) concentrations equal or greater than 75%, nor for digestate concentrations equal or greater than 50%. The seed germination index around 70% indicates a slight inhibition in the seed germination for initial mixture concentrations of 12.5%, 25% and 50%. However, the initial mixture concentrations of 12.5% and 25% served as inducer to the root growth, as can be seen in Fig. 7 (d), whereas the 50% concentration presented a growth lower than the one presented by the positive control. This slight inhibition in the seed germination and the reduced growth, for the 50% mixture concentration, may be explained by the presence of toxic substances in the sewage sludge that was used as inoculum. For the digestate concentrations of 12.5% and 25%, the germination index was around 80% which would be characterized as a non-significant value for germination. However, when looking at Fig. 7 (d) it is noted that root growth decreased gradually until the absence of growth in the 50% concentration of digestate. This episode may have occurred due to the presence of some AD products, Khanal et al. (2017), for example, state that in the AD of some industrial effluents, such as dairy, there is ammonia generation in high concentration, which could have affected the root growth. Toxicity studies for dairy wastewater are scarce in literature, so this is an important report for this effluent to study the phytotoxicity effects using the L. sativa seeds. But Tigini et al. (2016) evaluated pig slurry digestate through some ecotoxicity tests and found out that it was very toxic and needed a pretreatment before its use as fertilizer. This result was similar to the one obtained in the present work, as can be seen in Fig. 7 (c, d), where higher percentages of the digestate resulted in no germination, indicating the need of some pretreatment before the use of high digestate concentrations as biofertilizer. 4. Conclusions A system to monitor and control some variables of AD process was developed using some low acquisition cost sensors, actuators and the platform Arduino. This proposed system was designed as an alternative to higher cost equipment, improving the biogas production quality and decreasing management costs. The monitoring and control processes kept the desired pH and temperature conditions, demonstrating satisfactory results, and the total cumulative biogas yield achieved was 675.2 mL. It is critical to perform more tests for comparing the developed system efficiency with commercial reactors ones, as well as making the system scale up. Furthermore, it is interesting to monitor more variables, such as volatile fatty acids and gases like CO2 and H2S, as way of monitoring more closely the anaerobic digestion process and enhance the biogas production. Besides that, a deeper economic feasibility analysis is important to motivate future implementation of the developed system. Acknowledgments The authors would like to thank the Institute of Technology and

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