Anaerobic mono- and co-digestion of fruits and vegetable waste (FVW), slaughterhouse waste (SHW), and cattle manure (CM) under mesophilic conditions (35°C) were conducted through biochemical methane potential tests to investigate how the FVW in a co-substrate formulation improves the methane yield, the degradative synergy between substrates, and especially the stability of the process. The co-digestion of FVW:SHW and FVW:CM were evaluated with volatile solids (VS) ratios of 1:2, 1:1, and 2:1. The results indicated that the highest synergistic effect was found in the co-digestion FVW:CM at 1:1 VS ratio. However, the co-digestion FWV:SHW at 1:2 VS ratio increased the methane yield by 74.2% compared to the mono-digestion of FVW (776.3 mL CH₄ g VS_added⁻¹). As a critical condition in these processes, the stability was evaluated using the early warning indicator VFA/TA (volatile fatty acids/total alkalinity). The co-substrate SHW promotes greater stability in methane production as the soluble carbohydrate content in FVW increases. It was proposed that the high protein (49.04 ± 0.96% VS) and ammonia content (693 ± 3 mg L⁻¹) of SHW leads to the formation of a dampening system known as a carbonate-acetic buffer. It was concluded that balanced distribution between carbohydrates, proteins, and lipids is crucial to increase methane yields, and the low methane productions were associated with low N-NH₄⁺ concentrations (FVW:CM co-digestions). The results obtained in this study can serve as a basis to design full-scale digesters under similar operating conditions and with the same substrate:co-substrate relationships. Implications: The production of methane from the anaerobic digestion process of food, and lose waste presents a viable alternative of valorization and could help to mitigate environmental impacts. However, anaerobic digestion from these substrates carries high instabilities and low methane yields. The need to increase these yields and contribute to process stability must be considered in the selection of a co-substrate. In this context, this work aims to evaluate the best fruits and vegetable waste: co-substrateformulation, that promotes higher methane yield, a synergy between substrates, and to improve the AD process stability in the presence of perturbations in the substrate composition. We believe that our results could be helpful for the design processes for methane production from fruit and vegetable waste, to contribute to competitiveness with conventional energies and promote the sustainability of these processes.

Latin-American cities need efficient municipal solid waste (MSW) collection systems, specifically in municipalities with fast urbanization. Poor logistics in collection routes is one of the causes of inefficient MSW management. A strategy for designing the MSW collection routes, considering an environmental and economic analysis, was developed and tested in the Metropolitan Area of Monterrey (MMA), one of the cities with the highest urban and economic development in Mexico. Life cycle assessment and costs analysis were performed to evaluate archetypal routes’ environmental and economic impact in the current MSW collection system. The assessment of the environmental impact was realized through midpoint indicators of the ReCiPe 2008 method. The mathematical model describing the collection routes was developed using actual data from the MSW collection system in MMA, optimized by the Traveling Salesman Problem (TSP), and solved in RStudio. This study shows a reduction of between 9 and 20% of greenhouse gas emissions and distance covered in current collection stages, respectively. The results also demonstrate the influence of road direction, collection-truck efficiency, and traffic load in different stage on the environmental and economic impacts of the collection routes. A life cycle inventory, with realistic inputs and outputs of the MSW collection system, was developed. These inventories are scarce in Mexico and Latin America; their development helps designing cleaner MSW collection systems. Implications: The optimization of urban solid waste collection systems is a strategy to reduce costs and environmental impacts, considering factors such as distance or fuel consumption. However, mathematical models within the optimization require detailed information of the road network to achieve reliable results. In this context, Latin America shows difficulties in increasing its collection systems’ efficiency due to economic constraints and limited access to information from locals governments. The use of geographic information systems has been shown to collect real data on a city’s roads. This work aims to propose a strategy for designing urban solid waste collection routes with the least environmental and economic impacts through an optimization strategy that considers the road network’s real-time data, added to an environmental analysis developed with a life cycle assessment approach. We believe that our results could help the design of urban solid waste collection systems for cities with Latin American characteristics. Additionally, an inventory of specific inputs and emissions for waste collection in this type of city will be generated, supporting future sustainable management strategies.

This paper presents experimental and modeling data from a membrane-based wastewater treatment (WWT) pilot plant. The effluents from various upstream steps of a can-manufacturing plant were combined and subjected to a pretreatment process, which consisted of coalescing filters, coagulation and gravity settling, and sand activated carbon and polishing filtration, and a pressure-driven membrane process, such as reverse osmosis (RO). The performance of the RO membrane was evaluated and experiments were conducted using continuous wastewater flow. The complete membrane separation scheme was validated with a closed loop cell through several experiments, in which the concentration of the antiscaling agent and the pH were varied to determine the optimal operational conditions. Detailed parametric studies for these continuous flow experiments were conducted, and the permeate flow rates in the RO membrane system were experimentally measured. The experimental flow data were correlated and analyzed using an artificial neural network (ANN). A four-layer feed-forward network with a back-propagation algorithm was used to train the ANN models. After the training process was completed, the experimental flow data was used to assess the prediction capabilities of the networks based on the RO permeate water flow rate. This research showed that the RO unit results in the acceptable removal of 96.1% of the total dissolved solids and a maximum effluent recovery close to 72%. The predicted and experimental flow data were well correlated, and a determination coefficient between 0.97 and 0.99 was achieved.