A controller for directing a process primarily performed to control emission of a particular pollutant into the air, the process having multiple process parameters (MPPs) including a parameter representing an amount of the particular pollutant, wherein the process is either a wet flue gas desulfurization process (WFGD) or a selective catalytic reduction process in an air pollution control system that is either a wet flue gas desulfurization (WFGD) subsystem or a selective catalytic reduction subsystem, which receives SO 2 laden wet flue gas or NO x laden flue gas from a power generation system upstream of the wet flue gas desulfurization (WFGD) subsystem or the selective catalytic reduction subsystem, comprising: either a neural network process model or a non-neural network process model, the neural or non-neural network model representing a relationship between a first of the MPPs and one or more of the other MPPs, the one or more other MPPs including a second of the MPPs which is other than the parameter representing the amount of the emitted particular pollutant; and a processor configured with logic to estimate a value of the second MPP, and to direct control of the first MPP based on the estimated value of the second MPP and the neural or non-neural network model; wherein the second MPP is, if the process is a wet flue gas desulfurization process (WFGD), either (i) a pH level of matter applied in the process to absorb the particular pollutant and thereby control its emission into the air, (ii) a purity of a by-product produced in performing the process, (iii) an amount of oxygen dissolved in matter applied in the process to absorb the particular pollutant and thereby control its emission into the air, or, if the process is a selective catalytic reduction process either (iv) an amount of ammonia applied in the process to absorb the particular pollutant that is emitted into the air with the particular pollutant that is not absorbed, or (v) an amount of applied ammonia in exhausted reduced NO x flue gas, the particular pollutant being NO x, and the first MPP is an amount of the applied ammonia. either a neural network process model or a non-neural network process model, the neural or non-neural network model representing a relationship between a first of the MPPs and one or more of the other MPPs, the one or more other MPPs including a second of the MPPs which is other than the parameter representing the amount of the emitted particular pollutant; and a processor configured with logic to estimate a value of the second MPP, and to direct control of the first MPP based on the estimated value of the second MPP and the neural or non-neural network model; wherein the second MPP is, if the process is a wet flue gas desulfurization process (WFGD), either (i) a pH level of matter applied in the process to absorb the particular pollutant and thereby control its emission into the air, (ii) a purity of a by-product produced in performing the process, (iii) an amount of oxygen dissolved in matter applied in the process to absorb the particular pollutant and thereby control its emission into the air, or, if the process is a selective catalytic reduction process either (iv) an amount of ammonia applied in the process to absorb the particular pollutant that is emitted into the air with the particular pollutant that is not absorbed, or (v) an amount of applied ammonia in exhausted reduced NO x flue gas, the particular pollutant being NO x, and the first MPP is an amount of the applied ammonia.