Session

Session- Kinetics and Modeling

A Deductive Composting Kinetic Model

Bert Hamelers

Wageningen University, Netherlands

Kinetic models offer quantitative knowledge of the response of the composting material to changes in its composition or environment. This knowledge is crucial for proper design, control and operation of any composting operation. Two strategies are generally distinguished in model building, the inductive (data based) strategy and the deductive (theory based) strategy. The inductive strategy is at the basis of most if not all currently used kinetics models. Considering the lack of suitable measurements and the extensive measurement effort to cope with waste variability it is expected that the inductive strategy has reached its practical limits. The objective of this paper is to investigate the potential of a recently derived deductive analytical model to serve as a basis for a comprehensive kinetic model. The analytical model with its parameters will be described. A specific composting process is characterized by an associated specific parameter set. The effect of a process factor change on a specific process is described by a change in the values of the associated parameter set. Changes in process factors that are explicitly modeled can easily be accounted for. Examples of these so-called explicit process factors are oxygen and particle size. The effect of some process factors can only implicitly be accounted for. The most important process factor is temperature, which is not explicitly modeled, but influences the process by influencing parameters like growth rate and oxygen solubility. These implicit process factors need additional models to describe their relationship with the model parameters. Emphasis is given to these implicit process factors. The predictions of the deductive model are compared to the predictions of currently used kinetic models. By comparing to data sets from literature, strengths and weaknesses of both types of models are analyzed. The deductive model is very well able to handle the interaction of different process factors. Predictions can be better extrapolated. The main finding is that there is no such thing as a fixed set of optimal conditions, the optimal set is a function of the ever changing composition and structure of the waste. Management strategies failing to take this into account may give inappropriate control strategies.

Principles Of Composting Process Optimization

H M KEENER (1) , K Ekinci (1), D L Elwell (1), F C Michel, Jr (1).

(1) Food, Agricultural, and Biological Engineering, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio USA

, USA

Minimizing the cost of producing compost is a major goal today. This paper presents analytic expressions showing the interdependence between biological and physical factors for aerobic composting and the results of optimizations to minimize cost. Shown are how fan energy cost varies with compost density, porosity, resistance to airflow, decomposition rate, heat of decomposition, compost depth, air density, enthalpy of air (temperature, moisture), and compost maturity. Results assumed mixes for composting were correctly proportioned and blended and that process conditions (temperature, moisture, mixing schedule) were controlled. Equations defining the fixed cost of facilities as a function of the compost kinetic parameters and layout of the physical facilities are also presented. Kinetic data from studies from 1988-2002 on municipal solid waste (MSW), biosolids, short paper fiber (SPF), yard trimmings, food waste, broiler manure (BM), cage layer manure, hog manure, and dairy manure (over 50 studies) were presented and used in the derived equations to optimize system designs. Conclusions from those studies were: a. Compost maturity, i.e. reduction in organic matter, must be specified when calculating system efficiency as system cost increases significantly for higher levels of maturity. b. Cost of fan operation is 67% of that at 50 C if decomposition rate doubles from 50 to 60o C temperature. c. Selecting fan size to compost initially at temperatures 3-5oC above the optimum, although slowing the process initially, can be a cost effective approach to minimize fan size and power consumption, while minimally delaying maturity decomposition dropped. d. Sharing fans across windrows of different maturities reduces fixed and operating cost. e. Facilities cost can be minimize by: (1) avoiding bulking agent; (2) minimizing number of aisles and alleys; (3) maximizing cross sectional area; and (4) minimizing composting time. f. When composting, water loss per unit dry matter loss is approximately 2/3 of the theoretical value for continuous aeration. g. Composting materials above 81% moisture leads to increasing water content during composting based on 4.4 kgw/kgc lost. h. Highest rates of sustained water loss occur using continuous aeration. i. High ash or inert materials should use moisture based on an ash/inert free basis to formulate mixes. j. Optimum C/N for composting two or more products depends on decomposition rate as a function of C/N and bulk densities of materials. k. Ammonia loss is proportional to airflow in initial phase of composting.

Department of Biological and Environmental Engineering, Cornell University, USA

Composting is one of the most complex biotechnologies that man has appropriated from nature. There have been many successful applications of this biotechnology ranging from waste reduction to food production; while the biocomplexity that is innate to composting has often yielded myriad physical and biological states that are unexpected and, from an application standpoint, unwanted. Odor formation and incomplete stabilization are the manifestation of these unexpected states. What is there about this old and familiar biotechnology that still leaves us with so much uncertainty? The answer to the above question is the focus of this paper. The author explores the role of four factors, non-linearity, heterogeneity, uncertainty and adaptability in defining the myriad states of biocomplexity in composting. The non-linearity has been captured in numerous mathematical models that reveal the highly coupled non-linear relationship between the environmental state variables such as temperature, moisture content, pH, oxygen concentration, and how the dynamics of these state variables are driven by substrate degradation kinetics. Heterogeneity is most often noted in the chemical composition of the substrates, but is also manifested in the various interactions between the gaseous, aqueous and solid phases of composting. Uncertainty is observed in the high coefficient of variance reported in numerous studies. This variability is directly linked to our failure to identify and control additional state variable such as microbial population level. Also, the sensitivity of the non-linear coupling between state variables often requires tight control over these variables in order to reduce uncertainty. Finally, it is generally hypothesized that the heterogeneity or diversity of microbial community structure confers a certain level of resilience. This adaptability is reflected by the many myriad states of the microbial community structure that can evolve for identical environmental conditions and substrate chemical composition. However, a key question is whether these different community structures yield similar rates and extents of substrate degradation and exhibit similar microbial succession patterns.