A parameter value estimator for a process primarily performed to control emission of a particular non-particulate pollutant into the air, the process having multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted, 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 representing a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more of the other MPPs; and a processor configured with the logic to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the neural or non-neural network model; wherein the one 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 non-particulate 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 non-particulate 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 non-particulate pollutant that is emitted into the air with the particular non-particulate pollutant that is not absorbed, or (v) an amount of applied ammonia in exhausted reduced NO x flue gas, the particular non-particulate pollutant being NO x, and the one or more other MPPs include an amount of the applied ammonia. either a neural network process model or a non-neural network process model representing a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more of the other MPPs; and a processor configured with the logic to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the neural or non-neural network model; wherein the one 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 non-particulate 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 non-particulate 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 non-particulate pollutant that is emitted into the air with the particular non-particulate pollutant that is not absorbed, or (v) an amount of applied ammonia in exhausted reduced NO x flue gas, the particular non-particulate pollutant being NO x, and the one or more other MPPs include an amount of the applied ammonia.