Balancing multiple business and operational objectives within a
comprehensive control strategy is a complex configuration
task. Non-linearities and complex multiple process interactions
combine as formidable cause-effect interrelationships. A clear
understanding of these relationships is often instrumental to meeting
the process control objectives. However, such control system
configurations are generally conceived in a qualitative manner and
with pronounced reliance on past effective configurations (Foss,
1973). Thirty years after Foss' critique, control system configuration
remains a largely heuristic affair.
Biological methods of processing information are fundamentally
different from the methods used in conventional control
techniques. Biological neural mechanisms (i.e., intelligent systems)
are based on partial models, largely devoid of the system's underlying
natural laws. Neural control strategies are carried out without a pure
mathematical formulation of the task or the environment. Rather,
biological systems rely on knowledge of cause-effect interactions,
creating robust control strategies from ill-defined dynamic systems.
Dynamic modelling may be either phenomenological or
empirical. Phenomenological models are derived from first principles
and typically consist of algebraic and differential equations. First
principles modelling is both time consuming and expensive. Vast data
warehouses of historical plant data make empirical modelling
attractive. Singular spectrum analysis (SSA) is a rapid model
development technique for identifying dominant state variables from
historical plant time series data. Since time series data invariably
covers a limited region of the state space, SSA models are almost
necessarily partial models.
Interpreting and learning causal relationships from dynamic models
requires sufficient feedback of the environment's state. Systemisation
of the learning task is imperative. Reinforcement learning is a
computational approach to understanding and automating goal-directed
learning. This thesis aimed to establish a neurocontrol paradigm for
non-linear, high dimensional processes within an evolutionary
reinforcement learning (ERL) framework. Symbiotic memetic
neuro-evolution (SMNE) is an ERL algorithm developed for global tuning
of neurocontroller weights. SMNE is comprised of a symbiotic
evolutionary algorithm and local particle swarm optimisation. Implicit
fitness sharing ensures a global search and the synergy between global
and local search speeds convergence.
Several simulation studies have been undertaken, viz. a highly
non-linear bioreactor, a rigorous ball mill grinding circuit and the
Tennessee Eastman control challenge. Pseudo-empirical modelling of an
industrial fed-batch fermentation shows the application of SSA for
developing partial models. Using SSA, state estimation is forthcoming
without resorting to fundamental models. A dynamic model of a
multieffect batch distillation (MEBAD) pilot plant was fashioned using
SSA. Thereafter, SMNE developed a neurocontroller for on-line
implementation using the SSA model of the MEBAD pilot plant.
Both simulated and experimental studies confirmed the robust
performance of ERL neurocontrollers. Coordinated flow sheet design,
steady state optimisation and nonlinear controller development
encompass a comprehensive methodology. Effective selection of
controlled variables and pairing of process and manipulated variables
were implicit to the SMNE methodology. High economic performance was
attained in highly non-linear regions of the state space. SMNE
imparted significant generalisation in the face of process
uncertainty. Nevertheless, changing process conditions may necessitate
neurocontroller adaptation. Adaptive neural swarming (ANS) allows for
adaptation to drifting process conditions and tracking of the economic
optimum online. Additionally, SMNE allows for control strategy design
beyond single unit operations. SMNE is equally applicable to processes
with high dimensionality, developing plant-wide control
strategies. Many of the difficulties in conventional plant-wide
control may be circumvented in the biologically motivated approach of
the SMNE algorithm. Future work will focus on refinements to both SMNE
and SSA.
SMNE and SSA thus offer a non-heuristic, quantitative approach that
requires minimal engineering judgement or knowledge, making the
methodology free of subjective design input. Evolutionary
reinforcement learning offers significant advantages for developing
high performance control strategies for the chemical, mineral and
metallurgical industries. Symbiotic memetic neuro-evolution (SMNE),
adaptive neural swarming (ANS) and singular spectrum analysis (SSA)
present a response to Foss' critique.
