Abstract
To improve the financial sustainability and business management of composting facilities, mostly small-scale and manually operated facilities, the simultaneous optimization of the process and quality of the final product must be prioritized. This study used artificial neural networks (ANNs) and particle swarm optimization (PSO) as tools to evaluate the composting of biowaste (BW) with different cosubstrates (i. star grass (SG) and ii. a SG and sugarcane filter cake (SFC) mixture). The simulation aimed to maximize product quality in the shortest processing time by varying the mixing ratio and turning frequency. The simulation showed optimal conditions with a turning frequency of twice per week with the following mixtures: (i) BW:SG (72.9:27.1), 76 days of processing; and (ii) BW:SFC:SG (60:16:24), 88 days of processing. The results showed the effect of the type of carbon source in the cosubstrates on the retention time, which may imply the need for a larger area in composting facilities. On the other hand, the findings show that a minimum time is required to achieve a product that meets quality standards, although a longer processing time reduces the agricultural value of the compost. This model can be used to define design criteria and operating conditions and select a cosubstrate that can contribute to improving the agricultural quality of the final product.
Graphic Abstract
Similar content being viewed by others
Abbreviations
- ANN:
-
Artificial neural networks
- BW:
-
Biowaste
- MLP:
-
Multilayered perceptron
- MR:
-
Mixing Ratio
- R2 :
-
Coefficient of determination
- RMSE:
-
Root mean square error
- PSO:
-
Particle Swarm optimization
- SFC:
-
Filter cake
- SOM:
-
Self-organizing map
- TOC:
-
Total organic carbon
- TN:
-
Total nitrogen
- TP:
-
Total phosphorus
- TK:
-
Total potassium
- TF:
-
Turning Frequency
- SG:
-
Star Grass
References
Abdelhafez, A.A., Abbas, M.H., Attia, T.M., El Bably, W., Mahrous, S.E.: Mineralization of organic carbon and nitrogen in semi-arid soils under organic and inorganic fertilization. Environl Technol Innov 9, 243–253 (2018)
Lasaridi, K., Protopapa, I., Kotsou, M., Pilidis, G., Manios, T., Kyriacou, A.: Quality assessment of composts in the Greek market: the need for standards and quality assurance. J Environ Manage 80(1), 58–65 (2006). https://doi.org/10.1016/j.jenvman.2005.08.011
Zhou, H., Zhao, Y., Yang, H., Zhu, L., Cai, B., Luo, S., Cao, J., Wei, Z.: Transformation of organic nitrogen fractions with different molecular weights during different organic wastes composting. Biores Technol 262, 221–228 (2018). https://doi.org/10.1016/j.biortech.2018.04.088
Cesaro, A., Belgiorno, V., Guida, M.: Compost from organic solid waste: quality assessment and European regulations for its sustainable use. Resour Conserv Recycl 94, 72–79 (2015). https://doi.org/10.1016/j.resconrec.2014.11.003
Thi, N., Kumar, G., Lin, C.-Y.: An overview of food waste management in developing countries: current status and future perspective. J Environ Manage 157, 220–229 (2015). https://doi.org/10.1016/j.jenvman.2015.04.022
Campuzano, R., González-Martínez, S.: Characteristics of the organic fraction of municipal solid waste and methane production: a review. Waste Manage 54, 3–12 (2016). https://doi.org/10.1016/j.wasman.2016.05.016
Oviedo, R., Marmolejo, L., Torres, P.: Advances in research on biowaste composting in small municipalities of developing countries. Lessons from Colombia. Revista Ingenieria Investigacion y Tecnologia 18(01) (2017).
González, D., Colón, J., Gabriel, D., Sánchez, A.: The effect of the composting time on the gaseous emissions and the compost stability in a full-scale sewage sludge composting plant. Sci Total Environ 654, 311–323 (2019)
Iqbal, M., Nadeem, A., Sherazi, F., Khan, R.: Optimization of process parameters for kitchen waste composting by response surface methodology. Int J Environ Sci Technol 12(5), 1759–1768 (2015)
Bian, B., Hu, X., Zhang, S., Lv, C., Yang, Z., Yang, W., Zhang, L.: Pilot-scale composting of typical multiple agricultural wastes: parameter optimization and mechanisms. Biores Technol 287, 121482 (2019). https://doi.org/10.1016/j.biortech.2019.121482
Onwosi, C.O., Igbokwe, V.C., Odimba, J.N., Eke, I.E., Nwankwoala, M.O., Iroh, I.N., Ezeogu, L.I.: Composting technology in waste stabilization: on the methods, challenges and future prospects. J Environ Manage 190, 140–157 (2017)
Muscolo, A., Papalia, T., Settineri, G., Mallamaci, C., Jeske-Kaczanowska, A.: Are raw materials or composting conditions and time that most influence the maturity and/or quality of composts? comparison of obtained composts on soil properties. J Clean Prod 195, 93–101 (2018). https://doi.org/10.1016/j.jclepro.2018.05.204
Fernández, C., Mateu, C., Moral, R., Sole-Mauri, F.: A predictor model for the composting process on an industrial scale based on Markov processes. Environ Model Softw 79, 156–166 (2016)
Boem, F., Ferrari, R.M., Keliris, C., Parisini, T., Polycarpou, M.M.: A distributed networked approach for fault detection of large-scale systems. IEEE Trans Autom Control 62(1), 18–33 (2017)
Fernando, H., Surgenor, B.: An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. Robot Comput Integr Manuf 43, 79–88 (2017)
Díaz, M., Eugenio, M., López, F., García, J., Yañez, R.: Neural models for optimizing lignocellulosic residues composting process. Waste Biomass Valorization 3(3), 319–331 (2012)
Boniecki, P., Dach, J., Mueller, W., Koszela, K., Przybyl, J., Pilarski, K., Olszewski, T.: Neural prediction of heat loss in the pig manure composting process. Appl Therm Eng 58(1–2), 650–655 (2013)
Kujawa, S., Nowakowski, K., Tomczak, R.J., Dach, J., Boniecki, P., Weres, J., Mueller, W., Raba, B., Piechota, T., Carmona, P.C.R.: Neural image analysis for maturity classification of sewage sludge composted with maize straw. Comput Electron Agric 109, 302–310 (2014)
Soto-Paz, J., Alfonso-Morales, W., Caicedo-Bravo, E., Oviedo-Ocaña, E.R., Torres-Lozada, P., Manyoma, P.C., Sanchez, A., Komilis, D.: A New Approach for the Optimization of Biowaste Composting Using Artificial Neural Networks and Particle Swarm Optimization. Waste Biomass Valorization, 1–15 (2019).
Soto-Paz, J., Oviedo-Ocaña, E.R., Manyoma, P.C., Gaviría-Cuevas, J.F., Marmolejo-Rebellón, L.F., Torres-Lozada, P., Sánchez, A., Komilis, D.: A Multi-criteria decision analysis of co-substrate selection to improve biowaste composting: a mathematical model applied to Colombia. Environ Process 6(3), 673–694 (2019)
Soto-Paz, J., Oviedo-Ocaña, E.R., Manyoma-Velásquez, P.C., Torres-Lozada, P., Gea, T.: Evaluation of mixing ratio and frequency of turning in the co-composting of biowaste with sugarcane filter cake and star grass. Waste Manage 96, 86–95 (2019). https://doi.org/10.1016/j.wasman.2019.07.015
Montejo, C., Costa, C., Márquez, M.C.: Influence of input material and operational performance on the physical and chemical properties of MSW compost. J Environ Manage 162, 240–249 (2015). https://doi.org/10.1016/j.jenvman.2015.07.059
Zhang, L., Sun, X.: Influence of bulking agents on physical, chemical, and microbiological properties during the two-stage composting of green waste. Waste Manage 48, 115–126 (2016)
Chang, J., Chen, Y.: Effects of bulking agents on food waste composting. Biores Technol 101(15), 5917–5924 (2010)
de Guardia, A., Mallard, P., Teglia, C., Marin, A., Le Pape, C., Launay, M., Benoist, J.C., Petiot, C.: Comparison of five organic wastes regarding their behaviour during composting: part 1, biodegradability, stabilization kinetics and temperature rise. Waste Manage 30(3), 402–414 (2010)
Li, Z., Lu, H., Ren, L., He, L.: Experimental and modeling approaches for food waste composting: a review. Chemosphere 93(7), 1247–1257 (2013). https://doi.org/10.1016/j.chemosphere.2013.06.064
Zaborowicz, M., Wojcieszak, D., Górna, K., Kujawa, S., Kozłowski, R., Przybył, K., Mioduszewska, N., Idziaszek, P., Boniecki, P.: Determination of dry matter content in composted material based on digital images of compost taken under mixed visible and UV-A light. In: Eighth International Conference on Digital Image Processing (ICDIP 2016) 2016, p. 100332G. International Society for Optics and Photonics
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol Cybern 43(1), 59–69 (1982)
Beltramo, T., Ranzan, C., Hinrichs, J., Hitzmann, B.: Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm. Biosys Eng 143, 68–78 (2016). https://doi.org/10.1016/j.biosystemseng.2016.01.006
Barrena, R., Lima, F.V., Bolasell, M.A.G., Gea, T., Ferrer, A.S.: Respirometric assays at fixed and process temperatures to monitor composting process. Biores Technol 96(10), 1153–1159 (2005)
Bueno, P., Yanez, R., Rivera, A., Diaz, M.: Modelling of parameters for optimization of maturity in composting trimming residues. Biores Technol 100(23), 5859–5864 (2009)
Sharma, D., Yadav, K.D., Kumar, S.: Biotransformation of flower waste composting: optimization of waste combinations using response surface methodology. Biores Technol 270, 198–207 (2018). https://doi.org/10.1016/j.biortech.2018.09.036
Soobhany, N.: Assessing the physicochemical properties and quality parameters during composting of different organic constituents of Municipal Solid Waste. J Environ Chem Eng 6(2), 1979–1988 (2018). https://doi.org/10.1016/j.jece.2018.02.049
Ponsá, S., Pagans, E., Sánchez, A.: Composting of dewatered wastewater sludge with various ratios of pruning waste used as a bulking agent and monitored by respirometer. Biosys Eng 102(4), 433–443 (2009). https://doi.org/10.1016/j.biosystemseng.2009.01.002
Hemidat, S., Jaar, M., Nassour, A., Nelles, M.: Monitoring of Composting Process Parameters: a Case Study in Jordan. Waste and Biomass Valorization, 1–18 (2018).
Waqas, M., Nizami, A.S., Aburiazaiza, A.S., Barakat, M.A., Rashid, M.I., Ismail, I.M.I.: Optimizing the process of food waste compost and valorizing its applications: a case study of Saudi Arabia. J Clean Prod 176, 426–438 (2018). https://doi.org/10.1016/j.jclepro.2017.12.165
Kalemelawa, F., Nishihara, E., Endo, T., Ahmad, Z., Yeasmin, R., Tenywa, M.M., Yamamoto, S.: An evaluation of aerobic and anaerobic composting of banana peels treated with different inoculums for soil nutrient replenishment. Biores Technol 126, 375–382 (2012). https://doi.org/10.1016/j.biortech.2012.04.030
Puyuelo, B., Ponsá, S., Gea, T., Sánchez, A.: Determining C/N ratios for typical organic wastes using biodegradable fractions. Chemosphere 85(4), 653–659 (2011). https://doi.org/10.1016/j.chemosphere.2011.07.014
Cáceres, R., Malińska, K., Marfà, O.: Nitrification within composting: a review. Waste Manage 72, 119–137 (2018). https://doi.org/10.1016/j.wasman.2017.10.049
Hargreaves, J., Adl, M., Warman, P.: A review of the use of composted municipal solid waste in agriculture. Agric Ecosyst Environ 123(1), 1–14 (2008)
Lakhdar, A., Rabhi, M., Ghnaya, T., Montemurro, F., Jedidi, N., Abdelly, C.: Effectiveness of compost use in salt-affected soil. J Hazard Mater 171(1–3), 29–37 (2009)
Hurst, C., Longhurst, P., Pollard, S., Smith, R., Jefferson, B., Gronow, J.: Assessment of municipal waste compost as a daily cover material for odour control at landfill sites. Environ Pollut 135(1), 171–177 (2005)
Bryndum, S., Muschler, R., Nigussie, A., Magid, J., de Neergaard, A.: Reduced turning frequency and delayed poultry manure addition reduces N loss from sugarcane compost. Waste Manage 65, 169–177 (2017). https://doi.org/10.1016/j.wasman.2017.04.001
Jara-Samaniego, J., Pérez-Murcia, M.D., Bustamante, M.A., Pérez-Espinosa, A., Paredes, C., López, M., López-Lluch, D.B., Gavilanes-Terán, I., Moral, R.: Composting as sustainable strategy for municipal solid waste management in the Chimborazo Region, Ecuador: suitability of the obtained composts for seedling production. J Clean Prod (2016). https://doi.org/10.1016/j.jclepro.2016.09.178
Proietti, P., Calisti, R., Gigliotti, G., Nasini, L., Regni, L., Marchini, A.: Composting optimization: integrating cost analysis with the physical-chemical properties of materials to be composted. J Clean Prod 137, 1086–1099 (2016). https://doi.org/10.1016/j.jclepro.2016.07.158
Acknowledgements
The authors thank the Universidad del Valle for the financing of the investigation project CI 2985. Jonathan Soto-Paz thanks Colciencias for financing the National Doctorate-Convocatoria Doctorados Nacionales [National Doctorate Call] 727 - 2015. R. Oviedo-Ocaña thanks Universidad Industrial de Santander (UIS) for the support received during the development of this research.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Soto-Paz, J., Gea, T., Alfonso-Morales, W. et al. Co-composting of Biowaste: Simultaneous Optimization of the Process and Final Product Quality Using Simulation and Optimisation Tools. Waste Biomass Valor 12, 4489–4502 (2021). https://doi.org/10.1007/s12649-020-01321-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12649-020-01321-w