Environmental Engineering Reference
In-Depth Information
the process microbiology and biochemistry, the process engineering design
and the feedstock properties. Three scenarios are used to show how simple
one-component optimisation might be applied within a more complex multi-
component system, where the desired outcomes may be policy driven and
require optimisation of more than a single component.
6.2
Defining optimisation
To address the issue of optimisation of biogas yield relative to the feedstock
type, it is first necessary to consider what we mean by the term optimisation,
not only in the scientific sense but also in the wider context of AD as an
economically and environmentally sustainable
technology for waste
management and renewable energy production.
In the simplest sense, optimisation is the process carried out when we have
a range of parameters that can be controlled and a single variable that we
want to maximise or minimise: in the case of AD for energy production this
might be the biogas yield or, in the case of AD for waste management, the
quantity of residual waste solids for disposal. There is no lack of laboratory
studies applying this approach in experiments designed to provide a basis
for large-scale operating protocols. More commonly, however, there are two
or more parameters for which we want to achieve optimum values; for
example, we may want the highest possible biogas production from the
smallest possible digester, or the maximum energy yield with the minimum
operating costs. There may be multiple desirable outcomes and a range of
possible input parameters that could satisfy them. In AD it is very rare that
we formulate precisely what we want in a way that can be expressed and
solved mathematically. Very often we do not really know enough about the
systems that we are trying to design and operate, or the factors that
influence them, to have a high degree of control. The word optimisation is
therefore usually used in a fairly loose and non-mathematical sense to mean
something that gives us a result that is better than at least some of the
alternatives and in a range that we are happy with. In this chapter, we will
consider a few examples both of the simplest type of optimisation in which
we explore a range of parameters affecting one outcome and of the more
complex issues that have to be considered in multi-parameter optimisation.
The simple examples will be set in the context of the more complex problems
and different scenarios will be used for illustration.
As an example, consider the following two scenarios. Firstly, the waste
manager who receives daily deliveries of a waste feedstock and wants to
maximise the throughput of the plant in terms of wet tonnage per unit of
capital investment. The manager also has to show that the stability of the
final product meets regulatory requirements for reuse, recovery or disposal
and of course he wants the maximum biogas yield. Secondly, the energy
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