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Nowadays, the rapid decline of the environment caused by the generation of anthropogenic and industrial pollution is holding the attention of the scientific community. Systems of production need a new reconsideration to minimize solid, liquid, and gaseous effluents in order to preserve the ecosystems, along with sustainable development. The new tendency is wastewater valorization, which can be understood as the recovery of compounds from effluents with inorganic and organic contaminants. In this chapter, the bioelectrochemical transformation of contaminants in wastewater is described, taking into account the microbiology and electrochemical techniques. The chapter discusses wastewater treatment and the use or transformation of compounds recovered from wastewater. Moreover, technical aspects of using genomic, proteomics, and metabolomics approaches are examined taking into consideration the type of contaminant and the methodology for the transformation or recovery of products with added value and their possible incorporation into the industrial processes. Also, the addition of other type of effluents into the bioelectrochemistry process to obtain target products, such as bioenergetics, is reviewed with the consideration of the carbon footprint of the mentioned processes.

Since the industrial revolution period, the mining industry has brought benefits for human development. Mining is a primary industry relating several operations such as exploration, extraction, milling, purification, and refining of minerals. Oil industry is an example of mining with great importance in humanity. Metal and nonmetal industries also symbolize the principal activities with great impact on human development. However, this important industry has generated environmental impacts such as soil, water, and air contamination. Heavy metals and metalloids are bioaccumulative and cause several diseases including poisoning because these elements are accumulated in soft tissues of the body. Lead, cadmium, mercury, and arsenic are examples of heavy metals that cause poisoning in humans and other living beings. Depollution of contaminated sites with heavy metals and metalloids is necessary to ensure a high quality of life; moreover, it is part of the sustainable development. For the aforementioned purpose, in this chapter the technologies for the removal of heavy metals and metalloids generated from the mining industry are considered, with emphasis on the use of natural and synthetic zeolites. The latter type of zeolites is synthesized from wastes. In addition, the comparison between both types of zeolites for the removal of these elements is reviewed to identify the challenges in this important effort. Furthermore, the footprint carbon is estimated for these processes in order to evaluate the use of natural zeolites or those synthesized from wastes.

Crystallization processes are extremely important in pharmaceutical science since they affect both the solid-state properties of the drug substances, and the drug product stability and performance. The crystallization stage must guarantee the quality of the product by assuring high purity, desired particle size distribution and crystalline morphology. This paper presents the use of artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to model the complex process and identify the main parameters to optimize the crystallization of a specific pharmaceutical product in order to achieve substantial improvement in the quality of the product. Temperature, water content, volume, concentration and time addition of solvents, pH, and stirring speed were defined as inputs to build the ANN model to predict the crystal density. The ANN was able to learn the nonlinear relationships between structural information of the crystal and the main parameters of the process from an experimental set; the maximum mean absolute percentage error for the predicted values was 7.22%. The GA provided an optimal solution to define the operational conditions to take from a crystal density value of 0.61 to 0.737 g cm− 3 which represents a significant improvement in the physical and crystallographic properties. Experimental evaluations were carried out directly in the production plant obtaining crystal density values near the predicted one.

A theoretical model that describe the effect of design and operational conditions on current density distribution in a bioelectrochemical reactor used as microbial electrolysis cell (MEC) is described in this study. This model is proposed considering an approach where a direct electron transfer mechanism from the biofilm to the electrode surface takes place (mechanism present in most of microbial systems) and is governed by a dual donor-acceptor Nernst-Monod bioelectrochemical kinetic expression. The bioelectrochemical reactor is modelled considering two flow electrochemical reactor designs (a reactor design based in literature reports and a modified system proposed by the authors) operating at different flow inlet velocities and electrical overpotentials. Results obtained from the numerical solution shows that flow distribution is an essential aspect that impact the reactor performance, since concentration profiles and electrical potential-current distributions are strongly dependent on flow regime. Modified inlet configuration displays a more homogeneous fluid distribution and this behavior directly affects the mass transport and current density performance, as a result higher current density values are obtained for such configuration. Finally, it is expected that the information obtained from the analysis carried out in this report will provide us with a theoretical basis to realize the construction of a bioelectrochemical reactor prototype to develop the MEC concept.