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Novel settling characteristic metrics were developed based on the fundamental mechanisms of coagulation, flocculation, and settling. The settling metrics determined parameters that are essential in monitoring and optimizing the activated sludge process without the need for expensive or specialized equipment. Current settling characteristic measurements that don't require specialized instruments such as sludge volume index (SVI) or initial settling velocity (ISV), have no fundamental basis in solid-liquid separation and only indicate whether settling is good or bad without providing information as to limitations present in a sludge matrix. Furthermore, the emergence of aerobic granulation as a potential pathway to mitigate solid-separation issues further stresses the need for new settling characteristic metrics to enable integration of the new technology with the current infrastructure. The granule or intrinsic aggregate fraction in different types was of sludge was quantified by simulating different surface overflow rates (SOR). The technique named Intrinsic Settling Classes (ISC) was able to separate granules and floc by simulating high SOR values due to the lack of a flocculation time needed for granules. The method had to be performed in a discrete settling environment to characterize a range of flocculation behavior and was able to classify the granular portion of five different types of sludge. ISC was proven to accurately (±2%) determine the granule fraction and discrete particle distribution. The major significance of the test is its ability to show if a system is producing particles that will eventually grow to become granules. This methodology proved to be very valuable in obtaining information as to the granular fraction of sludge and the granular production of a system. Flocculent settling (stokesian) was found to be predominant within ideally operating clarifiers, and the shift to 'slower' hindered settling (non-stokesian) causes both failure and poor effluent quality. Therefore, a new metric for settling characteristics was developed and classified as Limit of Stokesian Settling (LOSS). The technique consisted of determining the total suspended solids (TSS) concentration at which mixed liquor settling characteristics transition from stokesian to non–stokesian settling. An image analytical technique was developed with the aid of MATLAB to identify this transition. The MATLAB tool analyzed RGB images from video, and identified the presence of an interface by a dramatic shift in the Red indices. LOSS data for Secondary activated-sludge systems were analyzed for a period of 60 days at the Blue Plains Advanced Wastewater Treatment Plant. LOSS numbers collected experimentally were validated with the Takacs et al. (1991) settling model. When compared to flux curves with small changes in the sludge concentration matrix, LOSS was found to be faster at characterizing the hindered settling velocity and was less erratic. Simple batch experiments based on the critical settling velocity (CSV) selection were used as the basis for the development of two novel parameters: threshold of flocculation/flocculation limitation (TOF/a), and floc strength. TOF quantified the minimum solids concentration needed to form large flocs and was directly linked to collision efficiency. In hybrid systems, an exponential fitting on a CSV matrix was proposed to quantify the collision efficiency of flocs (a). Shear studies were conducted to quantify floc strength. The methods were applied to a wide spectrum of sludge types to show the broad applicability and sensitivity of the novel methods. Three different activated sludge systems from the Blue Plains AWWTP were monitored for a 1 year period to explore the relationship between effluent suspended solids (ESS) and activated sludge settling and flocculation behavior. Novel metrics based on the transitional solids concentration (TOF, and LOSS) were also collected weekly. A pilot clarifier and settling column were run and filmed to determine floc morphological properties. SVI was found to lose sensitivity (r < 0.20) when characterizing ISV above a hindered settling rate of 3 m h-1. ISV and LOSS had a strong correlation (r = 0.71), but ISV was subject to change, depending on the solids concentration. Two sludge matrix limitations influencing ESS were characterized by transition concentrations; pinpoint floc formation, and loose floc formation. Pinpoint flocs had TOF values above 400 mg TSS L-1; loose floc formation sludge had TOF and LOSS values below 400 mg TSS L-1 and 900 mg TSS L-1, respectively. TOF was found to correlate with the particle size distribution while LOSS correlated to the settling velocity distribution. The use of both TOF and LOSS is a quick and effective way to characterize limitations affecting ESS. / Ph. D. / New parameters to determine how particles separate from water were developed. The new parameters didn’t require expensive or specialized instruments. Current parameters that don’t rely on specialized instruments give little information on how particles separate from water. The new parameters provide information on particle size, particle settling speed, and particle stickiness. The significance of this research is the ability for anyone in the field to gain a better understanding of the settling process they are monitoring using these parameters. The first parameter named Intrinsic Settling Classes allows one to determine the particle size distribution within a sludge mixture based on how fast the particles settle. The parameter requires dilution of the sample to inhibit the particles from sticking together. Bigger particles will settle faster, due to more mass assuming density does not change significantly. The second parameter named Limit of Stokesian Settling determines a particle’s settling speed. The parameter involved finding the maximum particles that could occupy a particular space before the particle-particle interaction start to hinder settling. Particles with a faster settling distribution can have more particles occupying a certain space before hindering one another. The third parameter named Threshold of Flocculation identified a particle’s stickiness. Stickier particles attach together in a shorter time resulting in large clumps of particles that settle faster. This parameter involved determining the minimum amount of particles needed to form large clumps of particles at a predetermined time. Stickier particles require less particles and time to form large clumps of particles.