Application of Optimization and Modeling for the Composting Process Enhancement
Abstract
:1. Introduction
2. Literature Search
3. Optimization of Composting Process
- Conventional (statistical or mathematical based) method:
- Iterative search techniques;
- Experimental design:
- Method based on response surface methodology;
- Methods based on factorial design;
- Methods based on Taguchi design.
- Non-conventional (artificial intelligence based) methods:
- Method based on fuzzy logic;
- Methods based on artificial neural networks;
- Methods based on metaheuristic algorithms:
- Genetic algorithm;
- Simulated annealing;
- Particle swarm optimization;
- Ant colony optimization;
- Tabu search;
- Artificial bee colony algorithm;
- Biogeography-based optimization;
- Teaching-learning based optimization.
- Methods based on expert systems.
3.1. Statistical Design of Experiments
- (i)
- experimental design;
- (ii)
- model development to describe the experimental data using statistics and regression analysis; and
- (iii)
- process optimization.
3.2. Application of Optimization Methodology in Composting Processes
4. Mathematical Modeling of Composting Process
4.1. Some Basic Principles of Composting Process Modeling
- (i)
- based on variables properties as deterministic (model variables are well known) and stochastic (model variables are random);
- (ii)
- based on deepened variable and their dependence on spatial position as lumped and distributed model;
- (iii)
- based on mathematical description of the process as continuous and discrete;
- (iv)
- based on mathematical structure as linear and non-linear.
- (i)
- mechanistic (white box) models are developed when all required data about process mechanisms are gathered;
- (ii)
- empirical (black box) models are developed when only experimental data are available and without understanding the mechanism in the process; and
- (iii)
- combined (gray box) models [77].
4.2. Application of Mathematical Modeling in Composting Process
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Composting Substrate | Process Variables | Optimization Method | Major Results | Reference |
---|---|---|---|---|
Municipal solid waste | Pollutant concentration (pyrene), soil/compost mixing ratio, compost stability expressed as respiration index | Response surface based on central composite design (CCD) | Experimental design ensures to obtain reliable values of optimal process conditions for pyrene biodegradation. High values of coefficient of correlations (R2 > 0.69) confirmed that second-order model was suitable for description of composting process. | [45] |
Municipal solid waste | Temperature, moisture content, oxygen flow rate, free air space | One-factor-at-a-time | Optimisation of the process conditions based on data gathered in the composting plant increases the performance of biological treatment plants form 53% to 107%. | [46] |
Municipal solid waste | Aeration, moisture, C/N ratio, time | Second order polynomials based on Box-Behnken central composite experimental design | Modelling results showed that composting time and C/N ratio are the most important variables influencing compost stability expressed as organic matter, chemical oxygen demand, nitrate concentration and biogedradability coefficient. R2 where gather than 0.8 for all analysed output variables. | [47] |
Pulp and paper mill sludge | Composting time, moisture, addition of hazelnut kernels | ANOVA test based on full factorial experimental design | 60% removal of ammonium from compost can achieved by performing the composting process under estimated optimal conditions (5 weeks, moisture of 50% and addition of hazelnut kernel of 25%). ANOVA showed that all input variables have significant effect on the process output variable. | [48] |
Palm oil mill effluent and empty fruit branches | Particle size, pH, initial carbon dosage, mix ratio of substrates | One-factor-at-a-time | The experimental conditions, the most suitable for effective composting process (electrical conductivity, protein content, organic matter content and C/N ratio), were estimated based on individual independent experiments. | [49] |
Cattle sultry and cattle manure with maize/silage and peach-juice pulp | pH, C/N ration and moisture content | Factorial optimization | Dewar autothermal assay was efficiently used to describe the relationship between process input variables (R2 = 0.8). The developed model gives information about the optimal composting conditions. | [50] |
Medlar pruning waste with and without cattle manure | Cattle manure addition | One-factor-at-a-time | Results showed that addition of cattle manure increased electrical conductivity, the total nitrogen content and organic-matter stability of the compost. | [51] |
Kitchen waste | Flay ash and bulking agent for moisture optimization Lime, temperature and inoculum size for C/N optimization | Response surface methodology based on Box–Benkhen design | Interaction between bulking agent and fly ash was significant for the compost moisture. Temperature, amount of lime addition and inoculum size have positive effect on C/N ratio of the compost. Second order polynomial equations described the experimental data with high precision; for moisture content R2 = 0.9975 and for C/N ratio R2 = 0.9947. | [15] |
Paper mill sludge and corn waste | Materia type (corn cob and corn husk), materia ratio, moisture, and process duration | Statistical optimization based on full factorial design | Additive dosage and composting duration had a positive effect on the removal of ammonia during co-composting of pulp/paper mill sludge and corn wastes. On the other hand, additive type and moisture content had negative effects on the ammonia removal. 91.84% of ammonia was removed when composting process was performed under optimal process conditions. | [52] |
Biodegradable solid waste | pH, moisture content, composting method | One-factor-at-a-time | When the moisture level was kept at 60% and composting was done in heaps and piles rather than pits and earthen pots, a long thermophilic phase was evident. | [53] |
Food waste | Natural zeoilte and biochar from agricultural waste addition | One-factor-at-a-time | Because of its strong affinity for cations, adding zeolites to composting materials lowers the concentration of heavy metals in the final products. The addition of biochar to the composting process increases nitrogen retention. | [54] |
Agricultural waste | Volatile solids, soluble BOD and CO2 evolution during composting process | Radial basis functional (RBF) neural network model | RBF neural network model was successfully implemented (R2 = 0.99) for planning of optimal agricultural waste mixture composition for composting process. Furthermore, the optimization based on the developed RBF model revealed that a waste combination of 70% vegetable waste, 20% cow manure, 10% sawdust, and 10 kg of dry leaves, coupled with 10 kg of dry leaves, give compost of preferable properties. | [55] |
Composting Substrate | Model Formulation | Model Description | Reference |
---|---|---|---|
Industrial waste | Mass balance for oxygen and heat balance | Partial differential equations for description of temperature and oxygen concentration distribution within the composting pile at steady state. Energy balance includes energy change due to air follow though composting pile, heat generated by oxidation and heat generated by biological activity. Oxygen mass balance includes oxygen concentration change due to air follow though composting pile and due to oxidation. | [78] |
Livestock manure | Hydrological model | Model describes the drainage of the waste water. In includes drainage from the composting pad area, direct precipitation falling and soil infiltration. Model uses weather data, data on vegetative filter strip buffer area data, soil infiltration rate, soil depth, water table depth, soli slope and plant type. | [79] |
Vegetable waste | Mass balances for energy, oxygen, vapor and liquid water concentration | Partial differential equations for description of temperature, oxygen concentration and minimum liquid water-to-compost weight ratio during the composting process. Energy balance includes energy change due to air flow through composting pile, heat generated by oxidation and heat generated by biological activity. Oxygen mass balance includes oxygen concentration change due to air flow through composting pile and due to oxidation. | [80] |
Bovine manure and sewage sludge | Organic N balance | Nitrogen mineralization kinetics in soils treated by different composts was analysed using five mathematical models: simple exponential model, double exponential model, special exponential model, hyperbolic model and parabolic model. Selected models allowed analysis of the mineralisation rate. | [81] |
Switchgrass and dog food mixture | Balances for substrate and oxygen concentration and energy transfer balance | Model describes the change of substrate concentration (sugars and starches) and oxygen concentration in a form of differential equations. Energy balance was expressed as partial differential equation. Model was able to predict the microbial populations (bacteria, fungi and yeast) growth reliably. | [82] |
Wood chips | Mathematical model of vertical moisture content movement | Mathematical model in form of differential equations, describes the dynamic of the moisture content change in a static composting pile. The amount of water in different composting layer dependent on input water flux, and output water flux by evaporation, diffusion, and percolation. | [83] |
Fresh cow manure, organic faction of municipal solid waste, non-digested sludge from municipal wastewater treatment | Exponential equations for description of organic matter settlement during composting | Two-parameter and three-parameter models were used. Two-parameter model includes parameter that represent the time when settlement reaches 50% and parameter that present maximum possible settlement. Three-parameter model additionally includes parameter that presents the inflexion point in the settlement curve. | [84] |
Green waste, biowaste and paper cardboard | Mass balances for organic matter change during composting | Mathematical model in form of differential equations describes the dynamics of organic matter change during the composting. Organic matter was analysed in five fractions (easily degradable soluble, slowly degradable soluble, hemicelluloses, cellulose, and lignin fractions). Microbial population growth rate was described by Monod kinetics. | [85] |
Household waste | Energy transfer balance | Mathematical model in a form of differential equations and empirical algebraic equations describes the heat transfer during composting. Model includes heat production that is proportional to oxygen consumption rate and heat transfers by evaporation, convection between material and gas crossing the material, conduction and surface convection between gas and material in bottom and upper parts of the reactor, | [86] |
Tobacco waste | Mass balance for substrate and biomass | Mathematical model in form of differential equations for description of biodegradation of organic matter from leachate which is produced during the composting process of tobacco waste. Applicability of Monod model, modified Monod model, Haldane model and expanded Haldane model for description of microbial growth was analysed. | [87] |
Sewage sludge, straw and sawdust | Artificial neural network model | Artificial neural network model for description of ammonia emission during composting based on selected input variables (sampling time, composting mixture temperature, composting mixture pH, composting mixture conductivity, weight of dry mass in composting mixture, C/N ratio of the composting mixture and ammonia nitrate content in composting mixture). Selected ANNs predicted the ammonia emission during composting with high precision R2 > 0.970. | [88] |
Sewage sludge, branches, grass clippings and ground leaves | Mass balances for organic carbon and mass balances for organic pollutants | Mathematical model in a form of differential equations describes organic pollutants and organic carbon dynamics during composting. Model includes two modules that can be used separately or coupled. Microbial population growth rate was described by Monod kinetics. | [89] |
Straw, yard waste, paper, wood fibers, screening residues and sewage sludge | Mass balances for organic micro-pollutants dynamics during composting | First-order (using a single degradation coefficient) and dual first-order (using two separate degradation coefficients) kinetic models were used to describe the degradation of organic micro-pollutants (representing the most common groups of organic chemicals present in sewage sludge) during composting. | [90] |
Olive mill waste | Heat balance equation, water mass balance, oxygen consumption and carbon dioxide production balances, compost volume change balance, biomass growt balance, nitrogen and phosphorous mass balances | Integrated model in a form of differential equations for decryption and prediction physicochemical and biological mechanism during composting. Insoluble organic matter hydrolysis was described using first-order kinetics. Microbial biomass growth was modelled with a double-substrate limitation (by hydrolyzed available organic substrate and oxygen) using Monod kinetics. The inhibitory factors of temperature and moisture content were included in the system. | [91] |
Sewage sludge and straw | Heat balance equation, organic matter balance, inert substrate balance, oxygen and carbon dioxide balances, water mass balance, biomass growt balance, ammonia mass balances | Integrated model in a form of 11 differential equations for decryption of aerobic composting dynamics. Model describes the growth of mesophilic and thermophilic microorganisms by Contois kinetics equation. Model also refers to easily hydrolysable organic matter and inert organic matter of the used composting substrate. According to model changes of carbon dioxide concentrations and ammonia concentrations are inverse to changes in the oxygen concentration. | [92] |
Poultry manure and wheat straw | Heat balance, substrate mass balance, oxygen mass balance, carbon dioxide mass balance, water mass balance | Integrated model composed of eight ordinary differential equations that take into account microbial kinetics, mass balance, heat balance and stoichiometry. The Monod kinetic model described the microbial growth and its dependency on moisture content, oxygen and temperature. | [93] |
Pig manure, poultry manure, wheat straw | Heat balance and oxygen mass balance | Oxygen uptake rate model in form of differential equations. Model takes into account oxygen consumption diffusion form gas phase to solid phase and oxygen consumption. Model also includes microorganisms growth according to Monod model and substrate hydrolysis in anaerobic conditions. | [94] |
Animal manure | Heat balance, carbon dioxide mass balance, water mass balance and ammonia mass balances | Model for simulation of physicochemical and biological mechanism during composting in pile based on four modules; biodegradation, nitrogen transformation and volatilization, thermal exchanges and free air space evolution. Microbial growth was described by first order kinetic. | [95] |
Sewage sludge and wheat straw | Heat balance, mass balances for moisture content and total mass | Integrated model for simulation of physicochemical and biological mechanism during composting. The model comprises first-order kinetics for organic matter (expressed as volatile solid) degradation, energy balance equation with two-dimensional heat transfer; and mass balance equation with one-dimensional mass transfer. Organic matter degradation includes adjusted functions of temperature, oxygen, moisture content and free air space. | [96] |
Wood chips and dog food | Mass balances for: aerobic biomass, oxygen, soluble substrate, insoluble substrate, water, temperature and inert matter | Integrated biomass-dependant dynamic model that takes into account temperature and biological growth under moisture, oxygen and substrate content. The aerobic biomass growth was described by the Monod model with dependence on oxygen concentration, soluble substrate concentration, moisture content and temperature. Model differs slowly and rapidly degradable substrates. | [97] |
Corn silage and cattle manure | Heat balance and substrate mass balance | Heat balance included biological heat production, heating of reactor system, heat of input and output gas, conductive and convective heat losses through reactor wall and, latent heat of water evaporation. Substrate degradation was described by first-order kinetics. | [98] |
Sawdust, wheat-straw, chicken manure | Exponential equations describing decomposition rate | Three kinetic models (first-order kinetics and exponential kinetics) were used to estimate the decomposition rate constant. Model 1 was a function of process temperatures, model 2 was a function of initial moisture content and the heat values, while model 3 was a function of process heat value and moisture content of the composting material. | [99] |
Agricultural waste | Mass balances for raw material, hemicellulose, lignin, soluble substrate, non-biodegradable volatile solids, and biomass | Model splits raw composting material into certain type of substrates (hemicellulose, lignin, soluble substrate, non-biodegradable volatile solids) and biomass. Model also refers to three groups of microorganisms (soluble substrate degraders, hemicellulose degraders, and lignin degraders). Hemicellulose and lignin hydrolysis were modelled with Contois kinetics. Soluble substrate uptake was modelled by Tessier kinetics. | [100] |
Organic fraction of municipal solid waste, organic fraction of municipal solid waste with orange peel waste, horticultural waste sewage sludge | Non-linear exponential model | Model for description of cumulative oxygen demand and odour emission during composting. The odour emissions generated during the composting processes were fitted to proposed model with high accuracy (R2 within the range 0.8–0.9). | [101] |
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Sokač, T.; Valinger, D.; Benković, M.; Jurina, T.; Gajdoš Kljusurić, J.; Radojčić Redovniković, I.; Jurinjak Tušek, A. Application of Optimization and Modeling for the Composting Process Enhancement. Processes 2022, 10, 229. https://doi.org/10.3390/pr10020229
Sokač T, Valinger D, Benković M, Jurina T, Gajdoš Kljusurić J, Radojčić Redovniković I, Jurinjak Tušek A. Application of Optimization and Modeling for the Composting Process Enhancement. Processes. 2022; 10(2):229. https://doi.org/10.3390/pr10020229
Chicago/Turabian StyleSokač, Tea, Davor Valinger, Maja Benković, Tamara Jurina, Jasenka Gajdoš Kljusurić, Ivana Radojčić Redovniković, and Ana Jurinjak Tušek. 2022. "Application of Optimization and Modeling for the Composting Process Enhancement" Processes 10, no. 2: 229. https://doi.org/10.3390/pr10020229
APA StyleSokač, T., Valinger, D., Benković, M., Jurina, T., Gajdoš Kljusurić, J., Radojčić Redovniković, I., & Jurinjak Tušek, A. (2022). Application of Optimization and Modeling for the Composting Process Enhancement. Processes, 10(2), 229. https://doi.org/10.3390/pr10020229