Influence of sludge reflux ratios on biodegradation performance in a coupled landfill leachate treatment process based on UASB and submerged MBR.

This study was undertaken to investigate the effects of different sludge reflux ratios (SRRs) on the overall performance and the fouling behavior of the up-flow anaerobic sludge blanket (UASB) reactor-anoxic-membrane bioreactor (MBR). The leachate and synthetic municipal wastewater were mixed in order to improve the biodegradability of the old leachate. Results showed that excellent removal efficiencies for chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were obtained by using the integrated UASB-anoxic-MBR process.

A multi-echelon supply chain model for municipal solid waste management system.

In this paper, a multi-echelon multi-period solid waste management system (MSWM) was developed by inoculating with multi-echelon supply chain. Waste managers, suppliers, industries and distributors could be engaged in joint strategic planning and operational execution. The principal of MSWM system is interactive planning of transportation and inventory for each organization in waste collection, delivery and disposal.

Waste management under multiple complexities: inexact piecewise-linearization-based fuzzy flexible programming.

To tackle nonlinear economies-of-scale (EOS) effects in interval-parameter constraints for a representative waste management problem, an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model is developed. In IPFP, interval parameters for waste amounts and transportation/operation costs can be quantified; aspiration levels for net system costs, as well as tolerance intervals for both capacities of waste treatment facilities and waste generation rates can be reflected; and the nonlinear EOS effects transformed from objective function to constraints can be approximated. An interactive algorithm is proposed for solving the IPFP model, which in nature is an interval-parameter mixed-integer quadratically constrained programming model.

An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds.

Eutrophication of small prairie reservoirs presents a major challenge in water quality management and has led to a need for predictive water quality modeling. Studies are lacking in effectively integrating watershed models and reservoir models to explore nutrient dynamics and eutrophication pattern. A water quality model specific to small prairie water bodies is also desired in order to highlight key biogeochemical processes with an acceptable degree of parameterization.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis.

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA.

An inexact reverse logistics model for municipal solid waste management systems.

This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM). Waste managers, suppliers, industries and distributors were involved in strategic planning and operational execution through reverse logistics management. All the parameters were assumed to be intervals to quantify the uncertainties in the optimization process and solutions in IRWM.

Radial-interval linear programming for environmental management under varied protection levels.

In this study, a radial-interval linear programming (RILP) approach was developed for supporting waste management under uncertainty. RILP improved interval-parameter linear programming and its extensions in terms of input reasonableness and output robustness. From the perspective of modeling inputs, RILP could tackle highly uncertain information at the bounds of interval parameters through introducing the concept of fluctuation radius.

Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem.

A hybrid inexact optimization approach for solid waste management in the city of Foshan, China.

An interval-parameter two-stage chance-constraint mixed integer linear programming (ITCMILP) model is provided for supporting long-term planning of solid waste management in the City of Foshan, China. The ITCMILP is formulated by integrating interval-parameter, two-stage, mixed integer, and chance-constraint programming methods into a general framework, and can thus deal with multiple uncertainties associated with model parameters, constraints and objectives. Three scenarios are examined, covering combinations of various system conditions and waste management policies.

Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty.

Nonpoint source (NPS) water pollution is one of serious environmental issues, especially within an agricultural system. This study aims to propose a robust chance-constrained fuzzy possibilistic programming (RCFPP) model for water quality management within an agricultural system, where solutions for farming area, manure/fertilizer application amount, and livestock husbandry size under different scenarios are obtained and interpreted. Through improving upon the existing fuzzy possibilistic programming, fuzzy robust programming and chance-constrained programming approaches, the RCFPP can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied.

Planning of municipal solid waste management under dual uncertainties.

Municipal solid waste management is a complex and multidisciplinary problem, involving a number of impact factors associated with various uncertainties. In this study, a hybrid interval-parameter possibilistic programming (IPP) approach was developed and applied for planning municipal solid waste management under dual uncertainties. The IPP improves upon the existing management approaches by allowing possibility distributions of the lower and upper bounds of some interval parameters in the objective function and interval information in the modelling coefficients to be effectively incorporated within its optimization.

A stepwise-cluster microbial biomass inference model in food waste composting.

A stepwise-cluster microbial biomass inference (SMI) model was developed through introducing stepwise-cluster analysis (SCA) into composting process modeling to tackle the nonlinear relationships among state variables and microbial activities. The essence of SCA is to form a classification tree based on a series of cutting or mergence processes according to given statistical criteria. Eight runs of designed experiments in bench-scale reactors in a laboratory were constructed to demonstrate the feasibility of the proposed method.

Multi-stage optimal design for groundwater remediation: a hybrid bi-level programming approach.

This paper presents the development of a hybrid bi-level programming approach for supporting multi-stage groundwater remediation design. To investigate remediation performances, a subsurface model was employed to simulate contaminant transport. A mixed-integer nonlinear optimization model was formulated in order to evaluate different remediation strategies.

An Interval Mixed-Integer Semi-Infinite Programming Method for Municipal Solid Waste Management.

This study proposed an interval mixed-integer semi-infinite programming (IMISIP) method for solid waste management under uncertainty. The uncertainty can be expressed as various constants, intervals, and functional intervals. The method is mainly based on the previous efforts on interval mixed-integer linear programming (IMILP) and semi-infinite programming.

Effects of sodium acetate as a pH control amendment on the composting of food waste.

A low level of microbial activity due to the production of organic acids is a recognized problem during the initial phase of food waste composting. Increasing such activity levels by adjusting the pH values during the initial composting phase was the primary concern to be investigated. In this study, sodium acetate (NaAc) was introduced as an amendment to an in-vessel composting system.

Adsorption of monorhamnolipid and dirhamnolipid on two Pseudomonas aeruginosa strains and the effect on cell surface hydrophobicity.

Previously, adsorption feature of a dirhamnolipid biosurfactant on diverse microbial cells was studied and the effect of the adsorption on cell surface hydrophobicity was compared. In this paper, the adsorption behavior of a monorhamnolipid and a dirhamnolipid on cells of two Pseudomonas aeruginosa strains was investigated in order to further reveal the influence of biosurfactant structure and cell property on the adsorption and the relation between the adsorption and cell surface hydrophobicity. Experimental results showed that the adsorption capacity of all the cells to monorhamnolipid was much stronger than to dirhamnolipid, and the rhamnolipid-sourced P.

Adsorption of dirhamnolipid on four microorganisms and the effect on cell surface hydrophobicity.

In this study, adsorption of dirhamnolipid biosurfactant on a Gram-negative Pseudomonas aeruginosa, two Gram-positive Bacillus subtilis, and a yeast, Candida lipolytica, was investigated, and the causality between the adsorption and change of cell surface hydrophobicity was discussed. The adsorption was not only specific to the microorganisms but also depended on the physiological status of the cells. Components of the biosurfactant with different rhamnosyl number or aliphatic chain length also exhibited slight difference in adsorption manner.

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management.

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced.

Minimax regret analysis for municipal solid waste management: an interval-stochastic programming approach.

In this study, an interval minimax regret programming (IMMRP) method is developed for the planning of municipal solid waste (MSW) management under uncertainty. It improves on the existing interval programming and minimax regret analysis methods by allowing uncertainties presented as both intervals and random variables to be effectively communicated into the optimization process. The IMMRP can account for economic consequences under all possible scenarios without any assumption on their probabilities.

PeLM: modeling of pesticide-losses through runoff and sediment transport.

A GIS-aided pesticide loss model (PeLM) was developed to simulate pesticide losses through surface runoff and sediment transport in watershed systems. The PeLM could tackle the movement of eroded soil along with surface runoff as well as the pesticide losses in adsorbed and dissolved phases. The contributions of different soil types in the sediment were also examined.

A two-stage interval-stochastic programming model for waste management under uncertainty.

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions.

Using multiple criteria decision analysis for supporting decisions of solid waste management.

Design of solid-waste management systems requires consideration of multiple alternative solutions and evaluation criteria because the systems can have complex and conflicting impacts on different stakeholders. Multiple criteria decision analysis (MCDA) has been found to be a fruitful approach to solve this design problem. In this paper, the MCDA approach is applied to solve the landfill selection problem in Regina of Saskatchewan Canada.